NVIDIA

You would build and maintain data pipelines for datacenter observability data and support capacity forecasting and hardware reliability analysis with infrastructure and data science teams. | The role requires a bachelor’s or master’s in computer science, 10+ years building large-scale data platforms, and proficiency in Python, SQL, and Spark or similar distributed processing frameworks. | NVIDIA’s Hardware Infrastructure organization is seeking a Senior Data Engineer to build and evolve analytics-ready data platforms that power observability, reliability analysis, and capacity forecasting for EDA datacenters. In this role, you will focus on transforming large-scale observability and telemetry data into trusted, well-modeled datasets that enable data scientists, analysts, and engineers to drive insights across global CPU and GPU compute clusters. We work closely with observability, infrastructure, and data science teams to ensure that data from EDA workloads and datacenter hardware is high quality, accessible, and optimized for analytical and predictive use cases. What You’ll Be Doing: Design, build, and maintain analytics-focused data pipelines that ingest, transform, and curate observability data from EDA datacenters Develop reliable ingestion pipelines for metrics, logs, traces, and hardware health telemetry generated by large-scale CPU and GPU clusters Partner with observability engineers to integrate data from tools such as Prometheus, Grafana, Elastic/OpenSearch, and Spark-based platforms into unified analytical datasets Model and organize data to support exploratory analysis, reliability modeling, forecasting, and long-term trend analysis Build and optimize batch and streaming workflows that support both near-real-time analytics and historical analysis Implement data quality checks, validation frameworks, and monitoring to ensure analytical accuracy and consistency Define data retention, aggregation, and enrichment strategies that balance analysis needs, system performance, and storage costs Enable self-service analytics by improving data discoverability, documentation, and usability Collaborate with data scientists and analysts to understand analytical requirements and evolve datasets to support new models and insights Continuously improve pipeline scalability, reliability, and performance as datacenter footprint and workload complexity grow What We Need to See: MS (preferred) or BS in Computer Science (or equivalent experience) or a related field with at least 10+ years of experience designing, building, and operating large-scale data pipelines and data platforms for distributed systems or infrastructure data Proficiency in Python and SQL, with experience supporting analytical and exploratory workloads Hands-on experience with distributed data processing frameworks such as Spark or similar technologies Familiarity working with observability and telemetry data, including metrics, logs, traces, and time-series data Experience designing data models and schemas that support flexible analysis and forecasting Ability to take ownership of data engineering initiatives and drive them end-to-end in collaboration with multi-functional partners Experience implementing data quality, validation, and monitoring for analytics pipelines Strong communication and collaboration skills, particularly when collaborating with engineering and infrastructure teams Adaptability in fast paced environments with evolving analytical and operational needs Ways to Stand Out from the Crowd: Experience supporting datacenter infrastructure analytics, hardware reliability programs, or workload performance analysis Familiarity with EDA workflows, HPC environments, or GPU-accelerated compute platforms Experience integrating or operating observability stacks (Prometheus, Grafana, Elastic/OpenSearch, Kafka, Spark, or similar tools) Background in large-scale distributed systems or data platforms A track record of improving analytics velocity and reliability through better data foundation #LI-Hybrid Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 27, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

You would create and deliver developer education for CUDA and use market insights and developer feedback to inform product strategy and roadmaps. | The role requires a postgraduate technical degree, at least five years of GPU compute experience, C++ and Python proficiency, and strong presentation skills. | Are you a computational scientist, engineer or data scientist passionate about working with developers? NVIDIA is seeking a Senior Technical Marketing Engineer (TME) to share the CUDA platform with our developer community. TMEs are drawn from outstanding software engineers and scientists who enjoy helping and teaching developers to use NVIDIA technology to make ground-breaking advances. We want someone who understands the GPU developer perspective and speaks their language. Your primary responsibilities will be to create and deliver technical content to teach developers how to use the CUDA platform successfully. You should be comfortable working in a dynamic environment and have experience with CUDA and NVIDIA GPU technologies. Your ability to work independently is important. We make heavy use of conferencing tools, but some travel is required for this role. You are empowered to find the best way to get your job done and do what it takes to make our developers successful. What you’ll be doing: Dig deep into new and important CUDA features, evaluate them and experiment with them to provide developer-centric insights to help inform product development. Craft compelling technical content, such as tutorials, guides, and blog posts to help enhance developer effectiveness using CUDA. Deliver technical workshops, webinars, and presentations to educate and empower developers to build innovative solutions using NVIDIA’s CUDA Platform. Capture market insights and developer feedback to advise product roadmap decisions and find opportunities for innovation and differentiation. Work effectively across NVIDIA with groups such as Product Management, Product Marketing, Engineering and Technical Sales to achieve business goals. What we need to see: MS or PhD in Engineering, Mathematics, Physical Sciences, or Computer Science or equivalent experience. 5+ years relevant work experience as a developer with a focus on GPU compute platforms, including competency in C++ and Python. Strong technical experience working with developer ecosystems. Excellent communication skills particularly in the presentation of highly technical material. Ability to work collaboratively across multi-functional teams. Self-motivated and independent with the ability to multi-task effectively with minimal day-to-day direction. Ways to stand out from the crowd: Contributions to open-source projects or involvement in developer communities. Author of published developer content like blogs, white papers, explainers, etc. Examples of innovative solutions or projects you've developed using CUDA Platform. Experience organizing or speaking at developer meetups, conferences, or hackathons. NVIDIA is renowned globally as a top-tier technology innovator and employer. Our team thrives on creativity, collaboration, and a shared drive to tackle ambitious challenges. If you're a technically-minded, developer-focused engineer eager to share the CUDA platform with our developers, we want to hear from you! With highly competitive salaries, a comprehensive benefits package, and an excellent engineering work culture, NVIDIA is widely considered to be one of the industry's most desirable employers. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 258,750 USD for Level 4, and 208,000 USD - 327,750 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 27, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Design and optimize microservices and data pipelines for autonomous-vehicle data and build workflows for data mining, AI training, and retrieval-augmented generation. | Requires 6–8+ years of relevant experience, a bachelor’s or postgraduate degree, and strong Python or Go skills plus proficiency in big data, distributed systems, and AI development. | NVIDIA is a global leader in high-speed computer vision, artificial intelligence (AI), and deep learning. Our team develops data engineering solutions that empower AI developers in autonomous vehicle (AV) domains to innovate quickly and effectively at scale. Are you ready to take on a senior technical role in building high-performance AI data pipelines? We seek an exceptional individual to design and optimize microservices and data pipelines to process massive volumes of AV data and enable seamless data mining and AI training. The ideal candidate will bring expertise in big data processing and distributed computing to create efficient solutions and overarching architectures for challenges such as video data curation, behavioral search, and AI dataset management. What you'll be doing: Scope and build tools, microservices, workflows, and distributed applications to accelerate data mining and AI training. Design and implement solutions for streaming, resilience, logging, security, authentication, workflow orchestration, and data management. Deploy AI models. Design and develop Retrieval-Augmented Generation (RAG) workflows enabling hybrid and agentic patterns. Analyze and operationalize complex distributed systems for speed-of-light performance. What we need to see: Experience developing high-performance, scalable software systems. MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field. Strong programming skills in Python or Golang Proficiency in key technologies like Kubernetes, Helm, Hive, Parquet, SQL, vector databases, e.g., Milvus. Strong architectural skills with a proactive, problem-solving mentality. Experience in data mining | AI development Experience building ETL pipelines and working with big data engines. Exceptional collaboration skills to work with system software and AI expert teams. Eagerness to learn and adopt new technologies such as NVIDIA RAPIDS. Ways to Stand Out from the Crowd: Prior experience with large-scale real-time streaming, augmented reality, or data curation. Prior background with Spark. Exposure to the latest advances in AI, including Large Language Models, Vision-Language Models, and Retrieval-Augmented Generation (RAGs). Innovative results, including patents, publications, or open source contributions. #AutonomousVehicles Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 26, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Design and operate scalable evaluation systems for open-source agent harnesses and benchmarks, diagnose agent failures, and deliver reliability and performance improvements with cross-functional teams. | Requires 8+ years of software engineering experience, strong Python and another language such as Rust or C++, LLM-agent and distributed-systems experience, and a degree in computer science, AI, or a related field. | NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. Are you excited about open-source software, developer tools, and the future of agentic applications? We are looking for a Senior Software Engineer to help build the core libraries, evaluation systems, and reusable harness capabilities that make AI agents safer, faster, and easier for developers to trust. In this role, you will work hands-on across open-source agent harnesses, sandboxed execution, model-provider infrastructure, and agent benchmarks to help improve the next generation of agents. You will turn agent-evaluation evidence into real improvements: diagnosing failures, contributing upstream, and proving quality, reliability, cost, and latency gains across models. Join our creative engineering team building foundational technology for the future wave of autonomous software systems! What you'll be doing: Define and evolve the technical approach for evaluating and improving open agent harnesses across frameworks, models, and benchmark environments. Build and operate scalable, reproducible evaluation systems spanning CI, scheduled compute, sandboxed execution, artifact provenance, trace capture, and analysis. Diagnose agent failures from traces, logs, tool results, and benchmark artifacts, separating harness, evaluator, environment, inference, and model causes. Design controlled experiments and promotion criteria that distinguish genuine harness improvements from noise, benchmark artifacts, or model-specific gains. Ship focused harness and runtime improvements, including upstream open-source contributions, detectors, regression tests, and maintainable documentation. Partner with Relay, Hermes, model, infrastructure, and research teams to turn recurring optimization needs into reusable runtime, trace, and evaluation capabilities. What we need to see: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Applied Math, or a related field, or equivalent experience. 8+ years of hands-on software-engineering experience, with demonstrated technical ownership of production systems; and architecture design leadership. Expert Python skills and working proficiency in Rust, Go, C++, or TypeScript, with the ability to debug systems across language and process boundaries. Experience building or extending LLM agents, coding agents, tool-use loops, model-provider integrations, developer tools, or evaluation infrastructure. Solid understanding of asynchronous execution, subprocesses, containers, sandboxing, callbacks, retries, networking, and distributed compute. Experience crafting reproducible experiments, benchmark methodology, performance investigations, regression gates, or reliability analysis under nondeterministic conditions. A record of shipping and maintaining open-source or developer-facing software with sound testing, API development, documentation, and code reviews. Ways to stand out from the crowd Meaningful contributions to open-source coding agents, harnesses, agent runtimes, evaluation frameworks, developer tools, or observability projects. Published research, technical writing, patents, or substantive open-source work in autonomous software engineering, agent evaluation, reliability, tool use, or inference efficiency. Experience with SWE-bench, Terminal-Bench, AgentBench, or comparable public agent benchmarks. Shipped improvements to context management, tool interfaces, retries, sandboxed repository execution, long-running agent loops, or evaluation environments. Experience with trace-analysis and observability systems such as OpenTelemetry, OpenInference, structured event pipelines, exporters, or debugging tools. Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/ Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 25, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Build and operate scalable infrastructure services for AI cloud deployments, with emphasis on automation, monitoring, and operational reliability. | Requires a bachelor's degree or equivalent experience, at least 8 years of relevant experience, strong Python and Go proficiency, and experience with cloud-native microservices, Kubernetes, and infrastructure automation. | NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. NVIDIA is seeking an experienced software engineer to join the Cloud Foundations Automation team. Our team builds and operates the core infrastructure services that power NVIDIA's DGX Cloud and SuperPod deployments, delivering secure, reliable, and observable platforms at global scale. What you'll be doing: Build and operate core infrastructure services that power NVIDIA's global AI infrastructure. Architect and develop secure, scalable and highly available cloud-native platform services. Develop software that enables infrastructure orchestration, self-service workflows, and platform automation. Own integrations with internal and external platforms to automate infrastructure provisioning and lifecycle management. Build observability and security capabilities that improve the reliability and resilience of our infrastructure. Partner with infrastructure and networking teams to deliver production services at scale. Drive operational excellence through automation, monitoring, incident response, and continuous improvement. What we need to see: BS or equivalent experience with 8+ years of relevant industry experience. Strong proficiency in Python and Go, with experience building production-quality software. Experience building cloud-native microservices and APIs on Kubernetes using frameworks such as FastAPI, gRPC, or REST. Experience with infrastructure automation (Terraform, Ansible), workflow orchestration (Temporal), and distributed systems using databases, Redis, and messaging platforms (Kafka, NATS, SQS). Experience designing, building, and operating production infrastructure services such as DNS, NTP, AAA (RADIUS/OAuth), and observability platforms. Strong Linux fundamentals with experience in observability (Prometheus, Grafana, OpenTelemetry, gNMI), networking (BGP, switching, routing, load balancing), and security (VPNs, firewalls, iptables/nftables). Excellent problem-solving, communication, and collaboration skills. Ways to stand out from the crowd: Hands-on experience with network infrastructure including switches, routers, and firewalls. Familiarity with InfiniBand, RDMA, and AI/HPC networking.Experience with NetBox, Nautobot, or similar network source of truth platforms. Contributions to open-source software. Experience with public cloud platforms. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 270,250 USD for Level 4, and 200,000 USD - 322,000 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 25, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Build and operate security controls and telemetry for compute, storage, and network infrastructure, investigate threats, automate enforcement, and collaborate on secure-by-default systems. | Requires 8+ years of infrastructure or security engineering, strong Linux depth, Python or Go fluency, experience securing large-scale compute fleets, and a bachelor's degree. | Securing NVIDIA's EDA clusters is not a matter of deploying a standard security stack. The workloads are too large, too long-running, and too performance-sensitive for an off-the-shelf playbook to simply apply. That constraint is the job. You'll be an individual contributor on Infrastructure Security Engineering for EDA Clusters, the team that implements security controls across the compute, storage, and networking that NVIDIA's hardware engineers design silicon on - spanning on-prem data centers and multi-cloud. You will sit alongside the engineers who run these clusters 24x7, close enough to deploy a control, investigate a behavior, and change the running environment yourself. This is hands-on build work, not control review. You'll decide where conventional tooling applies, where it actively cannot, and then engineer the alternative. If your depth is in large-scale compute, networking, or storage infrastructure at a hyperscaler, a security product company, or anywhere fleets and fabrics are the day job, and you've carried real accountability for the security properties of those systems, you're who we want to talk to. We don't expect fluency in every security product category. We expect you to own outcomes and pick up the remaining domain breadth fast. Security engineering here is engineering: code, instrumentation, and secure-by-default infrastructure - not tickets, exceptions, and spreadsheets. What You'll Be Doing: Build and operate security controls and telemetry for EDA compute, storage, and networking - design, ship to production, then own them. Engineer around hard constraints. Protect multi-day, high-value compute jobs without degrading them. Where heavyweight in-node agents are not viable, build the workable answer instead. Make the network and platform the control plane. Protection can't depend solely on what runs inside the node. Instrument for detection. Collect and normalize security signals from the fleet into our SIEM, and build the detections and dashboards that turn it into something actionable. Log collection alone is not monitoring. Investigate and respond. Chase down EDA-specific threats and exposure, and execute containment and remediation hands-on with infrastructure and service owners. Automate enforcement and evidence. Infrastructure-as-code and policy-as-code so secure configuration is the default state, with compliance evidence produced as an engineering output rather than a manual exercise. Run the lifecycle of the security technologies you own: selection, rollout, operation, and eventual replacement. Work across boundaries. Partner directly with data center operations, IT storage and networking, network engineering, CSP teams, and NVIDIA's central security organization on shared outcomes. Think like an adversary. Threat modeling and mitigation planning across network, storage, and compute. What We Need To See: 8+ years of hands-on infrastructure or infrastructure-security engineering across on-prem and cloud environments. Strong Linux depth - you are comfortable at the system level, not just the console level. Real experience operating or securing compute fleets at scale: provisioning, lifecycle, and the operational realities of long-running jobs. Working networking expertise applied to large compute environments. Programming/scripting fluency (Python, Go, or shell) used for automation, detection, and tooling you've shipped and maintained. Solid foundations in access control, authentication, segmentation, encryption, and vulnerability lifecycle. A track record of landing changes with partners who don't report to you. Clear written and verbal communication - credible with individual engineers and with leadership. A Bachelor's degree in a relevant engineering field or equivalent experience. Ways To Stand Out from the Crowd: Security or platform engineering inside EDA, HPC, or large research-cluster environments. Breadth across security domains - IAM, EDR/XDR, CNAPP, CSPM, zero-trust architectures - or a demonstrated pattern of picking up new domains fast. Built or operated infrastructure security capabilities at a hyperscaler or a security product company - you engineered detection and enforcement systems rather than deploying someone else's. Experience with kernel- and fabric-level observability and enforcement - eBPF, in-line and out-of-band inspection, DPU/SmartNIC offload - as an alternative to heavyweight in-node agents. Contributions to open-source security or infrastructure tooling. Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. We have some of the most forward-thinking and talented people in the world working for us and, due to outstanding growth, our world-class engineering teams are growing fast. If you're a creative and autonomous engineer with real passion for technology, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 24, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Own and architect data domains, pipelines, and products for marketing analytics and customer engagement, while leading cross-functional work and ensuring governance, reliability, and performance. | Requires 8+ years of data-engineering experience, proficiency in Python and SQL, distributed processing experience such as Spark, and a bachelor's degree; a technical master's and production data-platform experience are preferred. | NVIDIA's Digital Marketing Operations team is seeking a Senior Data Engineer to help build the shared data infrastructure that powers customer engagement, campaign intelligence, and marketing analytics at scale. Our data platform transforms customer behavioral signals, audience definitions, consent records, campaign metadata, and activation outputs into trusted, governed data products for marketing, analytics, personalization, campaign operations, and regional teams worldwide. These products enable audience segmentation, campaign performance measurement, privacy-compliant data activation, identity resolution, and the continued growth of NVIDIA's marketing technology ecosystem. We would love for you to apply today! What You'll Be Doing: Own a key data domain within Digital Marketing Operations, spanning pipelines, data products, audience services, and consumer teams. Shape its architecture and interfaces while anticipating needs related to scale, reliability, performance, privacy compliance, security, cost, and compatibility. Lead complex, cross-functional initiatives. Collaborate across engineering, marketing, and operations stakeholders to translate requirements into clear data architectures, write high-quality code in the critical path, and drive integrations safely into production. Architect, implement, and evolve batch and streaming pipelines that ingest, transform, reconcile, and serve customer behavioral, audience, campaign, consent, and activation data at scale. Build reliable, maintainable systems leveraging distributed processing frameworks, workflow orchestration tools, cloud data warehouses, object storage, and pub/sub messaging platforms. Build and evolve shared data quality frameworks and engineering standards that improve pipeline reliability, consistency, and efficiency across teams. Lead production incident investigations across pipelines, distributed processing, storage, and cloud services. Coordinate across system owners to establish root cause, drive resolution, and implement preventive improvements. Embed privacy and compliance by design across the data platform. Implement and enforce data governance, consent propagation, GDPR and PIPL compliance controls, data minimization, access boundaries, right-to-erasure workflows, and auditable data lineage as first-class engineering requirements, not afterthoughts. Establish robust data models, semantics, ownership boundaries, and serving interfaces across teams. Deliver trusted data assets that make customer and campaign data widely accessible to marketing consumers without sacrificing accuracy, privacy, or maintainability. Provide technical leadership through architecture and data model reviews, hands-on mentorship of junior engineers, and evidence-based resolution of difficult tradeoffs. Raise engineering quality across teams through reusable patterns, clear decisions, and sustained follow-through. What We Need To See: 8+ years of relevant industry experience with a Bachelor's or equivalent experience, and a Master's degree or equivalent in Computer Science, Engineering, or a related field. A sustained record of designing, implementing, and operating production data pipelines, data platforms, or distributed data systems. This includes end-to-end technical ownership of a multi-pipeline domain or a complex cross-team data engineering initiative. Proficiency in Python and one or more modern programming languages such as Java or Golang, with strong SQL fundamentals and the ability to write clean, testable, production-quality code. Deep hands-on experience with distributed data processing frameworks such as Apache Spark, and cloud-managed ETL services. Experience building and operating production pipelines with workflow orchestration tools such as Airflow or equivalent. Strong experience with cloud data platform services, including object storage, cloud data warehouses, managed transformation services, and related infrastructure. Demonstrated ability to architect and operate large-scale data workflows in a cloud environment. Experience with data lake and lakehouse architectures, including open table formats such as Apache Iceberg. Ability to design, manage, and optimize large-scale data storage for reliable analytical consumption. Strong SQL and NoSQL skills and data-modeling proficiency, including practical depth in query execution, incremental processing, schema evolution, consistency, analytical consumption patterns, idempotency, replay, late-arriving data, partial failure, and cross-system correctness. Experience building systems that move customer, behavioral, consent, campaign, or audience data across multiple platforms, with strong attention to data quality, governance, and operational reliability. Demonstrated skill in diagnosing production failures across distributed systems by analyzing logs, metrics, traces, query plans, and profiles, followed by implementing and validating long-lasting resolutions. Strong architectural judgment in assessing tradeoffs among reliability, performance, cost, privacy, compatibility, and maintainability. Experience guiding major migrations or architectural changes across teams without interrupting production service. Ways To Stand Out From The Crowd: Experience with MarTech stacks and B2C customer data at scale, including CDP integrations, reverse ETL, identity resolution, customer 360 or profile services, audience activation pipelines, campaign metadata, tracking code management, and multi-touch attribution. Experience designing and operating high-throughput, fault-tolerant streaming systems at scale, including exactly-once delivery semantics, consumer lag management, schema registry patterns, and replay strategies for large data volumes. NVIDIA is widely considered to be one of the technology world’s most desirable employers! We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 270,250 USD. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 23, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Architect and build global cloud-hosted compute services for AI agents and virtual desktop infrastructure, while leading technical strategy and partnering across teams. | Requires 15+ years in software, platform, or cloud engineering, deep AWS or Azure expertise, and a bachelor's or master's degree in computer science or a related field. | As a Principal Software Engineer, you will set the technical direction for NVIDIA's global Developer Compute platform. The platform covers AI agent sandboxes and virtual desktop infrastructure. You will architect and build the services that provision, stream, secure, and operate cloud-hosted compute environments at global scale across AWS and Azure. These environments serve both AI agents and the people who work alongside them. You will partner with engineering, IT, and security leaders across NVIDIA to turn AI and end-user workflows into reliable, high-performance platform capabilities. This role is for a hands-on technical leader who wants to define how agents and people get compute at the frontier of AI! What you will be doing: Leading contributions to the end-to-end architecture of NVIDIA's AI agent compute and virtual desktop platforms, from technical strategy and systems design through implementation, testing, and global deployment. Developing distributed compute services that provision and manage secure, isolated agent environments and virtual desktops across multiple clouds, availability zones, and regions. Driving the desktop streaming strategy, including protocol architecture, performance tuning, and end-user experience for both GPU-accelerated and general-purpose workloads. Collaborating with engineering, IT, and security leaders within NVIDIA to translate AI workflow and end-user compute requirements into a multi-year platform roadmap. What we need to see: Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent experience. 15+ years of experience in software, platform, infrastructure, or cloud engineering, including architecting, developing, and deploying global cloud compute infrastructure. Deep, hands-on experience with AWS and/or Azure compute, networking, identity, and storage services, such as EC2, VPC, Azure VMs, VNets, and Entra ID. Deep expertise in desktop streaming protocols such as Amazon DCV, PCoIP, or Omnissa Blast Extreme, including tuning for latency, bandwidth, and graphics performance. Experience designing and operating end-user computing solutions such as Amazon WorkSpaces, Azure Virtual Desktop, Windows 365, Citrix, or Omnissa Horizon. Experience with AI-native development and coding agents to multiply developer efficiency. Excellent communication skills and a proven track record to influence technical direction across teams and organizations. Ways to stand out from the crowd: Experience building platforms for AI agents, sandboxed execution, or computer-use agents that operate in full desktop environments. Experience with GPU virtualization, such as NVIDIA vGPU, and GPU-accelerated virtual workstations. A thorough understanding of zero-trust architecture, container and VM security, and multi-tenant isolation. Excited to contribute to a high-trust, collaborative culture while bringing a unique perspective to the team! We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and enjoy learning while having fun, then what are you waiting for? Apply today! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 248,000 USD - 391,000 USD. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 23, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

You would build and automate AI-ready developer documentation pipelines, encode documentation standards into workflows, and capture team knowledge for self-service. | The role requires 8+ years in technical writing or developer experience, hands-on docs-as-code and Python expertise, practical LLM use in content systems, and a bachelor's degree. | Build the systems that deliver Simulation Technology knowledge to developers, partners, internal teams, and the AI agents that increasingly sit between them and our documentation. Our KnowledgeOps team sets the information architecture and quality standards for Omniverse, Isaac Sim, and OpenUSD documentation. You'll build the automation, pipelines, and AI-ready infrastructure that apply those standards at scale, and work with documentation leads to refine the architecture based on what you learn. You're a writer who builds: as comfortable in Git, CI, or Python as in a style guide, and fluent with LLMs in daily work. We build systems where the right way is easier than the wrong way. For you, that means turning documentation, standards, and tribal knowledge into tooling, templates, and self-service paths, so fewer people have to ask "how do I…?" What you'll be doing: Collaborate with documentation leads on knowledge architecture, sharing implementation feedback (what works, breaks, and scales) to refine content models, information architecture, and AI-readiness patterns Build and run docs-as-code pipelines: Git authoring, CI/CD builds, linting, link and code-sample validation, and publishing Make knowledge AI-ready: structure and package content for RAG, internal assistants, and agentic workflows, including llms.txt, MCP-accessible sources, agent skills and context kits, and evaluation sets for answer accuracy Automate the content lifecycle with LLMs and scripting, from drafting and updating to auditing, with checks for freshness, code accuracy, and terminology drift, and human review where judgment matters Encode standards (user stories, release readiness, onboarding, security guidance) into templates, validators, and guided workflows rather than static documents Capture tribal knowledge from engineering, product, and operations, and turn it into playbooks, decision guides, and self-service paths Measure outcomes such as time-to-first-success, support deflection, repeat questions, and AI answer quality, and use the data to decide what to build, fix, or retire Partner with Engineering, Product, Security, Developer Relations, and Partner Programs so every release ships documented, discoverable, and supportable What we need to see: Bachelor's degree or equivalent experience, plus 8+ years in technical writing, developer documentation, or developer experience for complex software platforms Hands-on docs-as-code experience: Git, pull request review, Markdown/RST, static site generators (Sphinx, MkDocs, Docusaurus, or similar), and CI/CD Working proficiency in Python or similar to automate content tasks, transform data, and integrate with APIs Practical experience applying LLMs to content workflows (prompting, retrieval and grounding, output evaluation) and sound judgment about where AI can be trusted Information architecture skills: content modeling, taxonomy, and metadata for both navigation and machine retrieval Enough technical depth to read code and APIs, verify accuracy, and write working examples A systems mindset: you fix the class of problem, not just the instance A collaborative style: you turn design intent into working systems and give leads clear, constructive feedback grounded in implementation Ways to stand out from the crowd: Experience building RAG pipelines, AI assistants, MCP servers, or agent skill and context libraries backed by documentation Documentation experience in graphics, simulation, robotics, or real-time systems; familiarity with USD, Omniverse, Isaac Sim, or ROS Content quality automation you've built, such as Vale or custom linters, doc test harnesses, or freshness monitoring Measurable reductions in support load or onboarding time through self-service knowledge systems Open source contributions or published work on docs-as-code, AI-assisted documentation, or knowledge engineering Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 144,000 USD - 230,000 USD for Level 4, and 176,000 USD - 276,000 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 20, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Design, develop, and productize autonomous-driving perception software, integrate components, and deploy deep-learning models to embedded platforms. | Requires a relevant degree, at least 12 years of embedded real-time application experience, and strong C++, Python, test-driven development, and deep-learning framework skills. | NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. Now, NVIDIA’s GPU runs Deep Learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. We are now looking for a passionate Software Engineer to design, develop, and productize NVIDIA's autonomous driving solutions. As a member of our perception team, you will build world-class perception solutions. You will design and implement perception SW component and be challenged to improve robustness, efficiency, reusability of the software component working closely with the world class perception engineers and researchers. What you'll be doing: Execute full software development life cycle (SDLC). Write well-designed, testable code. Integrate perception (but not limited) software components into a fully functional software system. Develop software verification plans and quality assurance procedures. Document and maintain software functionality. Solve, debug and upgrade existing systems. Stay in sync with project plans and industry standards Deploy state-of-the art perception DNN models to embedded platforms. Analyze and improve the system using both real and synthetic benchmark data. What we need to see: BS, MS or PhD in Computer Science or related fields (or equivalent experience). Proven work experience (12+ years) on embedded platforms for real-time applications as a Software Engineer or Software Developer Strong programming skills in C++ and Python. Experience with test-driven development. Experience with disciplined, design based approach to software development. Proficiency in software engineering tools. Ability to document requirements and specifications. Outstanding communication and collaboration skills as we work as a tightly-knit team, always discussing and learning from each other. Ways to stand out from the crowd: Experience with CUDA / SIMD Hands on background in deploying DNN models to embedded platforms for real-time applications. Hands-on experience in using simulated / synthetic data to improve accuracy of machine learning models. Background with deep learning framework (e.g., TensorFlow, PyTorch, etc). Experience with integration and testing on real hardware / robotic platforms NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 20, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Analyze HPC applications on CPU and GPU systems, identify performance improvements, and advise compiler and application engineering teams. | Requires 8+ years of programming experience, strong Fortran/C/C++ and parallel programming skills, experience with HPC tools and runtimes, and solid mathematical foundations. | As a member of our team in NVIDIA's NVHPC compilers & tools group, you will analyze and run High Performance Computing (HPC) applications on HPC servers and systems to gain insight into the performance characteristics of these applications. The applications you'll work with range from small synthetic benchmarks that use a single core to full applications that utilize all of the resources on distributed-memory systems with heterogeneous compute nodes including CPUs, GPUs and many-core processors. In this role you will analyze these applications and identify optimization opportunities for compiler development teams and application engineering teams. What You’ll Be Doing • Assist customers GPU accelerate HPC applications. • Analyze High Performance Computing (HPC) applications to better understand their performance characteristics. • Provide advise and drive compiler and applications engineering development teams based on the analysis of these HPC applications. What We Need To See • BS/MS or equivalent experience in Computer Science or related engineering field. • 8+ Years of programming experience. • Solid understanding of Fortran/C/C++, as well as programming techniques, especially for parallel architectures; preferably for compilers • Experience with OpenACC, OpenMP, MPI, and CUDA. • Strong skills in performance analysis and tuning, as well as a broad understanding of parallel applications development tools and runtime environments. • Strong mathematical fundamentals, including linear algebra and numerical methods. • Understand performance considerations, tradeoffs and impact. • Expert interpersonal skills, logical approach to problem solving, good time management and task prioritization skills. Excellent written and verbal communication skills, along with the ability to work in a dynamic product oriented team. Ways To Stand Out From The Crowd • You have a deep understanding of machine architectures and micro-architectures. • Experience with debugging and porting as well as assembly language programming is a significant advantage. • Experience is leading and/or managing projects is a plus. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 18, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. , , JR2016206

Architect and lead the technical strategy for high-throughput distributed AI inference, GPU optimization, model lifecycle management, and production-scale serving across cloud and datacenter environments. | Requires 16+ years in technical roles, recent long-term AI infrastructure and large-scale inference experience, 7–10+ years of leadership, and deep expertise in GPU acceleration, low-level performance tuning, and distributed systems. | NVIDIA is leading the industry in delivering accelerated computing in cloud and enterprise environments. We’re a team of innovative engineers dedicated to solving some of the world’s biggest challenges, constantly driving advancements, and impacting millions of lives worldwide! As a technology leader at NVIDIA, you will lead the development of our global strategy for scaled-out AI inferencing. You will architect the high-throughput, low-latency distributed pipelines and model serving strategies required for massive scale and production reliability. You will define and drive the technical roadmap for full-lifecycle, from deployment and versioning to automated scaling, across enterprise and cloud environments. Working with NVIDIA leadership, you will establish the systems and orchestration layers that enable the world’s most advanced AI models to run with peak efficiency on our accelerated computing hardware. What You’ll Be Doing • Various Architectural Work: Architect distributed pipelines, define and drive the technical implementation of high-throughput, low-latency, distributed inference systems to support massive-scale AI workloads. • Collaborate on Cross Domain Disciplines: Hardware-software co-optimization, drive performance tuning at the kernel and driver level, optimizing GPU resource management and hardware acceleration for production-grade model serving. • Collaborate on Open Source and Ecosystem Projects: Guide and influence open source projects Dynamo, TensorRT-LLM, and ecosystem projects (vLLM, SGLang, Linux, Kubernetes, Ray) to bring state of the art inferencing on NVIDIA accelerated hardware. • Accelerate Integration: Orchestrate model lifecycles, lead the strategy for full-lifecycle model management, including automated deployment, versioning, and intelligent scaling across varied cloud and datacenter environments. • Engage Stakeholders: Collaborate with customers, infrastructure providers, and partners to ensure NVIDIA’s solutions set the industry standard for performance and availability. • Full Software and System Lifecycle: From ideation to architecture, design, development, deployment, operations, and full lifecycle management, lead all technical aspects of planning and continuous evolution of a large technical scope. What We Need To See • 16+ overall years in technical roles with a recent long-term focus on AI infrastructure and more recent direct experience in large-scale inference orchestration. Proven track record building secure, highly available, and durable production distributed systems. • 7-10+ years of leadership experience • BS/MS or higher or equivalent experience in systems / software engineering, or related engineering fields • Deep Technical Expertise: Proficiency in GPU architecture, hardware acceleration, and low-level performance tuning (CUDA, kernels) alongside cloud-native architectures for multi-tenant model serving. • Proven success delivering high-impact technically complex solutions that achieve high levels of transparency into resource utilization, performance, and operational insights. • Technical Leadership: Develop and advance consensus and organizational alignment across technical leadership and the highest level of senior corporate leadership. Ability to synthesize cross-functional needs into architecture and design while guiding internal execution across diverse teams. • Communication and Teamwork: Strong collaboration and influence skills, capable of leading engineering engagement, communicating with peers, partners, and working with high performance and accelerated computing customers. Ways To Stand Out From The Crowd • Application of Artificial Intelligence: Real world experience building the systems to support AI/ML workloads. • Industry Expertise: Direct experience in designing, developing, delivering and operating secure, highly available, scaled out systems in enterprise and cloud environments. • Engineering Enablement: Demonstrated history of creating scalable processes and extensible systems that facilitate cross-functional collaboration and operations at scale. • Open Source Collaboration: Familiarity with open source ecosystems and projects (e.g. Dynamo, TensorRT-LLM, vLLM, SGLang, Ray). Ability to collaborate and influence in open source project governance to represent NVIDIA, customers, and partners interests in technical alignment and direction. NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. We have some of the most forward-thinking and hardworking people on the planet working for us. If you're creative, passionate and self-motivated, we want to hear from you! With competitive salaries and a generous benefits package (www.nvidiabenefits.com), we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to outstanding growth, our best-in-class engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 320,000 USD - 488,750 USD. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until August 15, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. , , JR2023118

Lead NVIDIA’s strategy and architecture for large-scale AI inference systems, including GPU-optimized distributed pipelines, model lifecycle orchestration, and production operations. | Requires 16+ years in technical roles with recent AI infrastructure and large-scale inference experience, 7–10+ years of leadership, and deep expertise in GPUs, CUDA, low-level performance tuning, and cloud-native model serving. | NVIDIA is leading the industry in delivering accelerated computing in cloud and enterprise environments. We’re a team of innovative engineers dedicated to solving some of the world’s biggest challenges, constantly driving advancements, and impacting millions of lives worldwide! As a technology leader at NVIDIA, you will lead the development of our global strategy for scaled-out AI inferencing. You will architect the high-throughput, low-latency distributed pipelines and model serving strategies required for massive scale and production reliability. You will define and drive the technical roadmap for full-lifecycle, from deployment and versioning to automated scaling, across enterprise and cloud environments. Working with NVIDIA leadership, you will establish the systems and orchestration layers that enable the world’s most advanced AI models to run with peak efficiency on our accelerated computing hardware. What You’ll Be Doing • Various Architectural Work: Architect distributed pipelines, define and drive the technical implementation of high-throughput, low-latency, distributed inference systems to support massive-scale AI workloads. • Collaborate on Cross Domain Disciplines: Hardware-software co-optimization, drive performance tuning at the kernel and driver level, optimizing GPU resource management and hardware acceleration for production-grade model serving. • Collaborate on Open Source and Ecosystem Projects: Guide and influence open source projects Dynamo, TensorRT-LLM, and ecosystem projects (vLLM, SGLang, Linux, Kubernetes, Ray) to bring state of the art inferencing on NVIDIA accelerated hardware. • Accelerate Integration: Orchestrate model lifecycles, lead the strategy for full-lifecycle model management, including automated deployment, versioning, and intelligent scaling across varied cloud and datacenter environments. • Engage Stakeholders: Collaborate with customers, infrastructure providers, and partners to ensure NVIDIA’s solutions set the industry standard for performance and availability. • Full Software and System Lifecycle: From ideation to architecture, design, development, deployment, operations, and full lifecycle management, lead all technical aspects of planning and continuous evolution of a large technical scope. What We Need To See • 16+ overall years in technical roles with a recent long-term focus on AI infrastructure and more recent direct experience in large-scale inference orchestration. Proven track record building secure, highly available, and durable production distributed systems. • 7-10+ years of leadership experience • BS/MS or higher or equivalent experience in systems / software engineering, or related engineering fields • Deep Technical Expertise: Proficiency in GPU architecture, hardware acceleration, and low-level performance tuning (CUDA, kernels) alongside cloud-native architectures for multi-tenant model serving. • Proven success delivering high-impact technically complex solutions that achieve high levels of transparency into resource utilization, performance, and operational insights. • Technical Leadership: Develop and advance consensus and organizational alignment across technical leadership and the highest level of senior corporate leadership. Ability to synthesize cross-functional needs into architecture and design while guiding internal execution across diverse teams. • Communication and Teamwork: Strong collaboration and influence skills, capable of leading engineering engagement, communicating with peers, partners, and working with high performance and accelerated computing customers. Ways To Stand Out From The Crowd • Application of Artificial Intelligence: Real world experience building the systems to support AI/ML workloads. • Industry Expertise: Direct experience in designing, developing, delivering and operating secure, highly available, scaled out systems in enterprise and cloud environments. • Engineering Enablement: Demonstrated history of creating scalable processes and extensible systems that facilitate cross-functional collaboration and operations at scale. • Open Source Collaboration: Familiarity with open source ecosystems and projects (e.g. Dynamo, TensorRT-LLM, vLLM, SGLang, Ray). Ability to collaborate and influence in open source project governance to represent NVIDIA, customers, and partners interests in technical alignment and direction. NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. We have some of the most forward-thinking and hardworking people on the planet working for us. If you're creative, passionate and self-motivated, we want to hear from you! With competitive salaries and a generous benefits package (www.nvidiabenefits.com), we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to outstanding growth, our best-in-class engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 320,000 USD - 488,750 USD. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until August 15, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. , , JR2023118

Analyze HPC applications on CPU and GPU systems, identify performance optimizations, and guide compiler and application engineering teams. | Requires 8+ years of programming experience, strong C/C++ or Fortran and parallel computing expertise, HPC performance tuning, and knowledge of CUDA, OpenACC, OpenMP, and MPI. | As a member of our team in NVIDIA's NVHPC compilers & tools group, you will analyze and run High Performance Computing (HPC) applications on HPC servers and systems to gain insight into the performance characteristics of these applications. The applications you'll work with range from small synthetic benchmarks that use a single core to full applications that utilize all of the resources on distributed-memory systems with heterogeneous compute nodes including CPUs, GPUs and many-core processors. In this role you will analyze these applications and identify optimization opportunities for compiler development teams and application engineering teams. What You’ll Be Doing • Assist customers GPU accelerate HPC applications. • Analyze High Performance Computing (HPC) applications to better understand their performance characteristics. • Provide advise and drive compiler and applications engineering development teams based on the analysis of these HPC applications. What We Need To See • BS/MS or equivalent experience in Computer Science or related engineering field. • 8+ Years of programming experience. • Solid understanding of Fortran/C/C++, as well as programming techniques, especially for parallel architectures; preferably for compilers • Experience with OpenACC, OpenMP, MPI, and CUDA. • Strong skills in performance analysis and tuning, as well as a broad understanding of parallel applications development tools and runtime environments. • Strong mathematical fundamentals, including linear algebra and numerical methods. • Understand performance considerations, tradeoffs and impact. • Expert interpersonal skills, logical approach to problem solving, good time management and task prioritization skills. Excellent written and verbal communication skills, along with the ability to work in a dynamic product oriented team. Ways To Stand Out From The Crowd • You have a deep understanding of machine architectures and micro-architectures. • Experience with debugging and porting as well as assembly language programming is a significant advantage. • Experience is leading and/or managing projects is a plus. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 18, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. , , JR2016206

Analyze HPC applications across CPUs, GPUs, and distributed systems, then guide customer and engineering teams on performance optimization and GPU acceleration. | Requires a bachelor's or master's degree, at least eight years of programming experience, and strong HPC programming, parallel computing, mathematical, and performance-analysis skills. | As a member of our team in NVIDIA's NVHPC compilers & tools group, you will analyze and run High Performance Computing (HPC) applications on HPC servers and systems to gain insight into the performance characteristics of these applications. The applications you'll work with range from small synthetic benchmarks that use a single core to full applications that utilize all of the resources on distributed-memory systems with heterogeneous compute nodes including CPUs, GPUs and many-core processors. In this role you will analyze these applications and identify optimization opportunities for compiler development teams and application engineering teams. What you’ll be doing: Assist customers GPU accelerate HPC applications. Analyze High Performance Computing (HPC) applications to better understand their performance characteristics. Provide advise and drive compiler and applications engineering development teams based on the analysis of these HPC applications. What we need to see: BS/MS or equivalent experience in Computer Science or related engineering field. 8+ Years of programming experience. Solid understanding of Fortran/C/C++, as well as programming techniques, especially for parallel architectures; preferably for compilers Experience with OpenACC, OpenMP, MPI, and CUDA. Strong skills in performance analysis and tuning, as well as a broad understanding of parallel applications development tools and runtime environments. Strong mathematical fundamentals, including linear algebra and numerical methods. Understand performance considerations, tradeoffs and impact. Expert interpersonal skills, logical approach to problem solving, good time management and task prioritization skills. Excellent written and verbal communication skills, along with the ability to work in a dynamic product oriented team. Ways to stand out from the crowd: You have a deep understanding of machine architectures and micro-architectures. Experience with debugging and porting as well as assembly language programming is a significant advantage. Experience is leading and/or managing projects is a plus. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 18, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Build autonomous LLM-based red-team agents for reconnaissance, exploitation, and evidence capture, create verification harnesses, and mentor engineers. | Requires 12+ years in security engineering, including 4+ years in offensive security, strong Python, practical LLM-agent experience, and deep expertise in a web, cloud, or systems security domain. | NVIDIA's Product Security organization is looking for a Senior Offensive Security Engineer to push the frontier of AI-driven offensive security. We already run language model agents that audit source code and attack live web applications at fleet scale. These include multi-agent orchestration, adversarial verification, exploit-confirmation harnesses, and sandboxed execution. Your job is to make these agents meaningfully better attackers: new attack capabilities, new target classes, higher true-positive rates, and safer autonomy. This is a hands-on role for someone who is both a strong offensive security practitioner and a builder of agentic systems. Join us! What you'll be doing: Crafting and building new red team agents: autonomous and semi-autonomous LLM agents that perform reconnaissance, hypothesis-driven exploitation, and evidence capture against NVIDIA-owned targets Extending our existing agent harnesses (multi-phase orchestration, parallel specialist subagents, judge/verifier stages, out-of-band callback infrastructure) across multiple agent runtimes and model providers Encoding real operator tradecraft into prompts, tools, and playbooks — web app exploitation, auth bypass, SSRF/deserialization chains, cloud and Kubernetes attack paths — so agents find what a skilled human would Building the verification layer: exploit-confirmation harnesses that prove findings are real before a human ever sees them, driving false-positive rates down Engineering the safety side of autonomy: sandboxing, scope enforcement, tool allowlists/blocklists, credential isolation, and prompt-injection defenses for agents operating with offensive capability Evaluating and benchmarking frontier models for offensive tasks; partnering with our human red team to turn their engagements into repeatable agent capabilities Mentoring engineers on the team and setting the technical bar for agentic offensive tooling What we need to see: Bachelor's degree or equivalent experience 12+ years in security engineering, with at least 4 years in offensive security: penetration testing, red teaming, exploit development, or vulnerability research Deep, hands-on exploitation skill in at least one domain (web application, cloud/Kubernetes, or systems/binary) — you can build and prove an exploit chain, not just run a scanner Strong software engineering ability in Python; you ship production code, not just PoCs Practical experience building with LLMs: agent frameworks, tool use/function calling, multi-agent orchestration, or coding agents (Claude Code, Codex CLI, or similar) Sound judgment about autonomous offensive tooling — scoping, authorization, and blast-radius thinking are second nature Respectful, responsible approach to offensive testing — we break things carefully and fix them fast Ways to stand out from the crowd: Published security research, CVEs, conference talks, or notable CTF results Certifications like OSWE, OSEP, OSCP, or GXPN Experience with SAST/DAST internals, fuzzing, or program analysis Experience red-teaming AI systems themselves (prompt injection, jailbreaks, model evaluation) or contributing to AI-security research Familiarity with Kubernetes/OpenShift, GitOps, and operating worker fleets at scale With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and talented people in the world working for us and, due to unprecedented growth, our elite engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 18, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Provide on-site support for AI infrastructure deployments, troubleshoot complex hardware and systems issues, and create repeatable diagnostic guidance with engineering and partner teams. | Requires a technical bachelor's or master's degree and at least five years of large-scale systems or data-center troubleshooting, with proficiency in Linux and hardware infrastructure. | NVIDIA is looking for a Senior Field Applications Engineer to assist with AI Factory build implementation and swift assistance alongside OEM and deployment collaborators. This position offers practical, on-site technical support for initial deployments, critical field challenges, preparation shortfalls, and repeated build issues that need focused technical resolution. You will collaborate with NVIDIA engineering, services, architecture, program, account, field, and partner-facing teams to identify issues, shorten resolution times, validate diagnostic flows, and transform field findings into repeatable guidelines. This role suits a senior technical contributor who can work onsite, provide clarity during critical issues, and help establish a more consistent approach for AI Factory build-outs. This is a field-facing role with encouraged onsite support during critical build, bring-up, validation, and blocking issue windows. What you'll be doing: Deliver direct field assistance for AI Factory build implementation, encompassing First Time Deployments, deployment bring-up, validation, and issue triage with OEM and deployment partners. Serve as a senior technical resource for urgent field issues involving customer concerns across rack, server, GPU, networking, storage, firmware/BMC, Linux, cabling, power, thermal, and deployment-tooling domains. Deploy to partner or field sites as needed to support targeted rapid response, critical issue closure, and execution risk reduction. Drive structured triage for deployment blockers, including failure reproduction, data collection, domain isolation, quality of critical issue packets, and owner alignment. Help reduce time to resolution by validating diagnostic flows, improving test practices, and detailing repeatable troubleshooting methods. Support bone-pile review and reduction by helping classify failures, identify repeat patterns, and connect field data to root cause and corrective actions. Capture First Time Deployment lessons learned and update guidelines, readiness criteria, and execution playbooks for reuse across OEM engagements. Provide concise leadership access to issue status, risk, blockers, decision needs, and recovery plans during active deployments. What we need to see: Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, Computer Science, Systems Engineering, or a related technical field (or equivalent experience). 5+ years of experience in data center deployment, rack integration, technical field support, manufacturing support, partner services, infrastructure bring-up, or large-scale systems troubleshooting. Practical experience troubleshooting intricate infrastructure problems related to servers, GPUs, switches, storage, firmware/BMC, Linux, cabling, optics, power, thermal, or deployment tooling. Experience working directly with partner, customer, OEM, ODM, system integrator, or deployment services teams in field or production environments. Strong troubleshooting in the field, including rigorous debug, clean data collection, reproduction quality, and clear handoff of critical issues. Ability to operate onsite in fast-paced deployment environments while communicating clearly with engineering, field teams, partner teams, and leadership. Strong written and verbal communication skills, including the ability to summarize complex technical issues into executive-ready status and risk updates. Travel required (up to 50%) to support field engagements, partner sites, and deployment activities. Ways to stand out from the crowd: Experience with AI Factory, accelerated computing, rack-scale AI infrastructure, large-scale GPU systems, or HPC deployments. Background with NVIDIA GPUs, NVIDIA networking, InfiniBand, RoCE, high-speed Ethernet, or large-scale cluster bring-up. Experience crafting or improving diagnostic flows, test procedures, readiness playbooks, blocking issue templates, or best-practice libraries. Experience with bone-pile analysis, RMA/failure pattern review, root cause feedback loops, or systemic quality improvement. Strong Linux, scripting, automation, telemetry, or log-analysis skills. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 132,000 USD - 207,000 USD for Level 3, and 160,000 USD - 253,000 USD for Level 4. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 18, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Build and operate infrastructure services and automation, define reliability practices, participate in on-call response, and advise peer teams on systems design. | Requires a related bachelor's degree or equivalent, 8+ years of relevant experience, Python or Go coding, infrastructure automation, distributed systems, production cloud systems, Linux, and containers. | NVIDIA is looking for a Systems & Software Engineer interested in building and running reliable large scale infrastructure platform services. In this role you will create and support automation that manages infrastructure, as part of our organization running EDA platforms atop of NVIDIA hardware. What You’ll Be Doing • Design, build, deploy, and run infrastructure services & manage the software life cycle in scope to meet our business goals • Build and maintain automation to eliminate manual toil and scale output without scaling effort • Participate in the definition of our internal facing service level objectives and error budgets as part of our overall observability strategy • Practice sustainable blameless incident prevention and incident response while being a member of an on-call rotation • Consult with and provide consultation for peer teams on systems design best practices What We Need To See • BS degree in Computer Science or a related technical field involving coding (e.g., physics or mathematics) or equivalent experience • Experience coding in Python and/or Go. Note: you must be able to produce code without AI assistance during the interview process. • Experience with infrastructure automation and distributed systems design • Experience developing tools for running large scale private or public cloud systems in production • In depth knowledge of Linux systems and Containers • A track record showing a good balance between initiating your own projects, convincing others to collaborate with you and collaborating well on projects initiated by others. • 8+ years of relevant experience Ways To Stand Out From The Crowd • Systematic problem-solving approach, coupled with strong communication skills and a sense of ownership and drive. Experience accelerating positive impact to the business using coding assistant(s), MCP servers, or AI agents • In-depth knowledge of networking and storage technologies. Orchestrating existing automated and manual processes using Continuous Integration and/or workflow engines • Experience with duplicating or emulating existing complex production infrastructure into staging/test environments. Experience working with or developing bare metal as a service (BMaaS) associated systems • Experience working with or developing multi-cloud infrastructure services and running private or public cloud systems based on one or more of Kubernetes, OpenStack, Docker or Slurm. Experience working with Nvidia GPU-based systems NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. Our invention serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA is seeking exceptional individuals like you to help us drive the next wave of artificial intelligence. NVIDIA is considered one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us. If you are creative and autonomous, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 15, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. , , JR2024972

Lead a global customer engineering organization and Kubernetes platform team while improving EDA infrastructure support through automation, AI assistance, and operational performance standards. | Requires 12+ years building and leading engineering support organizations, 5+ years leading globally distributed teams, and deep familiarity with Kubernetes, EDA workloads, job schedulers, and AI infrastructure. | Every NVIDIA GPU starts as a job on our internal EDA infrastructure. Thousands of hardware engineers designing next-generation silicon depend on it being fast, available, and frictionless and when it isn’t, tapeout schedules feel it. This team is the difference. We’re hiring a hands-on engineering leader to develop our Global Customer Engineering for Internal EDA Infrastructure - the organization sitting directly between our chip designers and the platforms they run on. This is a multi-team leadership role spanning managers and tech leads across a globally distributed organization, and it comes with two co-equal mandates: A world-class customer engineering organization for EDA infrastructure - reactive when it must be, proactive by design. The Kubernetes platform team that delivers one of our customer services end to end. You will design software solutions, and automation pipelines to eliminate operational overhead. When encountering recurring toil for customer engineering, your immediate impulse is to engineer permanent automated remedies. Staying directly connected to live production environments is essential hence the end-to-end Kubernetes platform responsibility. What You’ll Be Doing • Build the leaders. Grow and develop the leaders running a globally distributed team of customer-facing engineers who own the reactive support queue for EDA infrastructure - escalating into service engineering with clean, high-quality signal. • Develop the Kubernetes platform. Run the K8s platform team as a first-class customer engineering service: availability, roadmap, developer experience, and the operational excellence behind it. • Ship the L0 AI support layer. Own the evolution of our AI support agent from FAQ bot to a system that safely resolves real issues against real internal systems - and measurably removes volume from human queues. • Set the bar. Define and defend SLOs for operational performance, and win genuine stakeholder buy-in. • Stay close to the metal. Drive deep support and continuous improvement across EDA job schedulers (LSF/Slurm), Kubernetes platforms, and the internal LLM harnesses our chip designers depend on. What We Need To See • 12+ overall years building and leading engineering support organizations, spanning reactive and proactive customer engineering. • 5+ years of experience leading a globally distributed organization, including developing managers and leaders. • Proven ability to influence technical roadmaps by turning support trends into engineering requirements that actually ship. • Deep technical familiarity with Kubernetes, EDA workloads, job schedulers, and AI/LLM infrastructure foundations. • Track record of leveraging automation and AI/ML to improve support efficiency and drive ticket volume down. • Exceptional communication skills - credible with individual engineers and executive leadership in the same day. • A Bachelor’s degree in a relevant engineering field or equivalent experience. Ways To Stand Out From The Crowd • Led a platform team and a customer facing team simultaneously, and made each one better because of the other. • You’ve architected “self-healing” support ecosystems where a customer engineering team builds and owns the tools it runs on including “shift-left” tooling that resolved issues before a ticket ever existed. • You’ve led the full lifecycle of generative AI / LLM-based support assistants, well beyond basic chatbots, into systems that integrate deeply and safely with internal infrastructure. • Run LSF, Slurm, or Kubernetes at a scale where manual intervention was the bottleneck and you personally led the automation that removed it. NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. Our invention serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA is seeking exceptional individuals like you to help us drive the next wave of artificial intelligence. NVIDIA is considered one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us. If you are creative and autonomous, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD for Level 4, and 320,000 USD - 488,750 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 15, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. , , JR2025101

Build and optimize distributed reinforcement-learning post-training infrastructure, including training and rollout workflows, runtime frameworks, and fault-tolerant scaling. | Requires 5+ years in distributed systems or ML infrastructure, strong Python and C/C++, and substantial production-scale systems experience, with depth in RL post-training, PyTorch distributed training, Kubernetes internals, or distributed systems design. | Reinforcement learning post-training is driving some of the most significant capability gains in AI today. It is the process that teaches a model to reason through hard problems, follow complex instructions, and act as an autonomous agent. It is also one of the hardest infrastructure challenges in the field. RL requires inference, rollout generation, and training running in a continuous loop. The rollout step is what makes it hard: the model must interact with environments, tools, and other models to produce the signal that drives learning. Coordinating actor, critic, and reward models across heterogeneous hardware at scale pushes the limits of what distributed systems can do. NVIDIA is building an RL Frameworks engineering team to develop the open-source tools and infrastructure that AI researchers and post-training teams depend on. The team spans the full software stack, from collaborating closely with the researchers and labs pushing the frontier, to contributing to RL frameworks like VeRL, Miles, and TorchTitan, to improving the distributed runtimes they depend on, including Ray and Monarch. Whether your strength is working with researchers to understand and address their need optimizing deep learning frameworks, or building distributed infrastructure, we want to hear from you. Come join us to build the systems that enable the next generation of AI. What You Will Be Doing You will architect and build RL post-training infrastructure that scales efficiently from experimentation on a single GPU to production across thousands of nodes. This means tuning RL training-inference-rollout loops on GPUs, CPUs, and LPUs for performance where it matters, contributing to and improving the performance and usability of open-source RL frameworks, and partnering with the teams who own them. The role also spans fault tolerance, elastic scaling, and fast restarts so long-running distributed training jobs survive failures, stragglers, and resource contention. Beyond GPU-accelerated training, this work includes partnering with teams building CPU-driven rollout workloads, including tool-use, code execution, and agentic environments, supplying the systems and framework engineering needed to run them efficiently alongside GPU- or LPU-accelerated generation and GPU-accelerated training. It also means advocating for researcher and partner needs with NVIDIA's networking, math library, and compiler teams so the capabilities RL workloads require get prioritized and delivered, and working with hardware teams to take advantage of next-generation hardware capabilities in post-training workloads. What We Need To See • MS or PhD in Computer Science, Computer Engineering, or a related field (or equivalent experience) • 5+ years of professional experience in distributed systems, high-performance computing, deep learning infrastructure, or ML systems engineering • Strong proficiency in Python and C/C++ • Demonstrated experience building or contributing to large-scale distributed systems or runtime frameworks in production at a frontier AI lab, hyperscaler, or major technology company • Strong verbal and written communication skills and the ability to collaborate across organizational and geographic boundaries Depth in one or more of the following technical areas: • Reinforcement learning for LLM post-training (RLHF, PPO, GRPO, DPO, reward modeling), including how algorithms map to distributed execution and the systems challenges they create (heterogeneous placement, rollouts, environment execution, resharding between training and generation) • PyTorch internals, including distributed training primitives (FSDP, tensor parallelism, pipeline parallelism) and their composition • Kubernetes runtime internals (container lifecycle, pod scheduling, resource quotas, GPU allocation) • End-to-end distributed systems design (service boundaries, data flows, consistency models, failure modes, recovery approaches) Experience in any of the following areas is a plus: • Deep expertise in networking (NCCL, NVLink, InfiniBand), advanced multi-dimensional parallelisms (Megatron-LM, FSDP2, TP/DP/PP, MoE), or memory optimizations (quantization-aware training, mixed precision) • Experience integrating high-performance inference engines (vLLM, SGLang, TensorRT-LLM) into RL training loops for GPU-accelerated rollout • Strong background in actor- and task-based distributed programming (Ray, Monarch, or comparable systems) • Familiarity with multi-turn training, multi-agent co-evolution, or VLM post-training Ways To Stand Out From The Crowd • Open-source contributions to RL post-training or distributed training projects (e.g., VeRL, Miles, TorchTitan, OpenRLHF, NeMo-Aligner, DeepSpeed-Chat), including significant work on framework internals where applicable • Kubernetes work beyond routine operations (custom operators, GPU device plugins, or scheduling contributions) • Direct experience operating frontier-scale training (RL post-training at thousands of GPUs and/or large-scale LLM or multimodal pre-training) • Hands-on experience with production distributed failures at scale (stragglers, resource contention, hardware faults) Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/ Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until August 25, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. , , JR2015863