TetraScience

Own and scale AI and data infrastructure, build cloud-based services and MLOps workflows, and guide technical design while mentoring engineers and maintaining platform reliability and security. | Requires 10+ years in software and infrastructure engineering, experience scaling distributed systems, production LLM systems, expert TypeScript and Python, and multi-tenant platform experience. | Who We Are TetraScience is the Scientific Data and AI Cloud company. We are catalyzing the Scientific AI revolution by designing and industrializing AI-native scientific data sets, which we bring to life in a growing suite of next gen lab data management solutions, scientific use cases, and AI-enabled outcomes. TetraScience is the category leader in this vital new market, generating more revenue than all other companies in the aggregate. In the last year alone, the world's dominant players in compute, cloud, data, and AI infrastructure have converged on TetraScience as the de facto standard, entering into co-innovation and go-to-market partnerships. In connection with your candidacy, you will be asked to carefully review the Tetra Way letter, authored directly by Patrick Grady, our co-founder and CEO. This letter is designed to assist you in better understanding whether TetraScience is the right fit for you from a values and ethos perspective. It is impossible to overstate the importance of this document and you are encouraged to take it literally and reflect on whether you are aligned with our unique approach to company and team building. If you join us, you will be expected to embody its contents each day. The Role We're looking for a Lead Software Platform Engineer working at the intersection of distributed systems and MLOps. You will own and scale our AI and data infrastructure as a product our customers build their science on, and that every other engineering team builds against. You will architect the cloud-based services and MLOps infrastructure that enable production-grade AI/ML workflows, working closely with Applied AI engineers, data engineers, and platform teams, and you will act as the technical design authority for how models, LLMs, and agents run in production. The work spans the model and prompt lifecycle, the evaluation and observability harness, the security and tenant boundaries around prompts and retrieval, and the cost and latency controls that keep production AI economically viable at scale. This is a highly impactful seat for an engineer who has shipped AI/ML infrastructure as a multi-tenant product rather than internal tooling. The model serving, MCP, and agent capabilities you build are consumed directly by scientists at the world's largest pharmaceutical companies, running against their own data under their compliance obligations. If that scope appeals to you, and you thrive on turning ambitious scalability and cost targets into concrete technical strategy inside a regulated environment, we'd love to talk to you. What You Will Do Own the technical architecture of the AI/ML platform: the service and API surface our customers use to run models and agents against their own scientific data, and that Applied AI and data engineering teams build against internally. Own the end-to-end model and prompt lifecycle across Databricks MLflow and AWS Bedrock, including registration, versioning, asset bundles, staged promotion, rollback, and multi-model serving. Design the inference substrate for both real-time and batch AI workloads, including routing, batching, caching, concurrency control, GPU and accelerator capacity planning, handling of large binary inputs such as instrument images, and graceful degradation under load. Integrate AI models and large language models (LLMs) into production systems using architectures like retrieval-augmented generation (RAG), and architect the agentic layer above them: tool and function calling, MCP-based tooling, and agent runtimes, deciding what belongs in the platform versus in the applications built on top of it. Design security into the AI platform rather than leaving it to the applications above it, partnering with our security team on guardrails, prompt-injection and tool-abuse defenses, PII and PHI handling, and hard tenant data boundaries across prompts, retrieval, and tool calls. Build the evaluation and quality infrastructure that makes AI shippable: offline and online eval harnesses, golden datasets, regression gates in CI, A/B and shadow deployment, and drift and hallucination detection in production. Establish observability for the AI platform, including monitoring, alerting, logging, and distributed tracing, and set the SLI, SLO, and SLA model for systems whose outputs are probabilistic. Design for reproducibility and lineage required in a validated pharma environment, with versioned data, code, prompts, and model artifacts, and an audit trail that can withstand customer and regulatory scrutiny. Contribute to the infrastructure-as-code and deployment automation for the AI platform (CloudFormation, AWS CDK), partnering with the team that owns production deployments to support multi-tenant infrastructure, online upgrades, and on-demand compute allocation. Own production readiness for the AI platform with Applied AI engineers, data engineers, and platform teams: the performance, reliability, and cost-efficiency of models in production, plus incident response and runbooks. Act as SME and design authority across product and engineering. Lead design reviews, write the reference architectures and technical documentation others follow, and mentor senior and mid-level engineers on distributed systems and AI engineering practice. Set technical direction on emerging AI infrastructure: evaluate new frameworks, serving runtimes, model providers, and data types, and make clear build-versus-buy decisions grounded in cost, risk, and scalability. 10+ years of professional experience in software engineering and infrastructure engineering, with a proven track record of designing, building, and scaling distributed, cloud-native systems in production. Demonstrated experience as a technical leader or architect, accountable for the key decisions on system design, scalability, performance, and cost optimization. Experience designing security into a multi-tenant platform, including authorization boundaries between tenants and handling of sensitive data such as PII and PHI, with awareness of LLM-specific risks like prompt injection and tool abuse. Extensive experience building and maintaining AI/ML infrastructure in production, including model deployment and lifecycle management, delivered as a multi-tenant product with external users rather than internal tooling for a single team. Candidates whose experience is limited to building pipelines for their own team will not be a fit. Deep, hands-on experience taking LLM-based systems to production, including RAG architecture, retrieval and embedding design, prompt and model versioning, and tool or function calling. Not just prototyping with an SDK. We are looking for someone who has operated these systems under real traffic, real latency budgets, and real failure modes. Expert-level coding skills in TypeScript and Python building robust APIs and backend services, with the judgment to critically evaluate AI-generated code for correctness, security, and architectural fit. Production-level experience with a model registry and serving stack, ideally Databricks MLflow, including model registration, versioning, asset bundles, and serving workflows. Experience treating AI evaluation as a release gate rather than post-hoc reporting, including eval harnesses, regression gates, and drift or quality monitoring for non-deterministic systems. Proficiency in API-first design, including REST and OpenAPI specifications, designing APIs that are scalable, secure, versioned, and extensible. Solid working knowledge of AWS and containerized workloads (e.g., Docker), and familiarity with infrastructure-as-code such as CloudFormation or CDK, with the ability to contribute to CI/CD pipelines and deployment automation. Experience defining observability and SLI/SLO/SLA practice for production systems, including monitoring, alerting, and distributed tracing. Ability to articulate ideas clearly to customers and cross-functional teams, influence technical direction on teams you do not manage, and mentor other engineers. Nice to Have Familiarity with emerging LLM frameworks for advanced prompt orchestration and programmatic LLM pipelines. Experience running agentic or multi-step orchestration in production, and with MCP as an integration surface for tooling and non-human identities. Understanding of LLM cost monitoring, latency optimization, and usage analytics in production environments, including per-tenant cost attribution for tokens, GPU, and inference. Experience with multimodal model inputs, including image and instrument data, and the throughput and cost implications of serving them at scale. Experience with fine-tuning, distillation, or model optimization such as quantization, batching, or KV-cache strategy, to improve latency and cost. Experience delivering ML or AI systems in a regulated or validated environment (GxP, 21 CFR Part 11, SOC 2), including computer system validation and audit readiness. Background in scientific, life sciences, or laboratory data domains. 100% employer-paid benefits for all eligible employees and immediate family members Unlimited paid time off (PTO) 401K Flexible working arrangements - Remote work Company paid Life Insurance, LTD/STD A culture of continuous improvement where you can grow your career and get coaching The salary range for this position is $200K-$270K USD. The salary range posted reflects our target baseline for this role. Final compensation is determined by a thorough evaluation of factors including the candidate’s specific experience, localized market data, and internal team equity. We are not currently providing visa sponsorship for this position.

Design and build bidirectional integrations and scientific data products for biotech and pharmaceutical customers while mentoring engineers and collaborating across teams. | Requires 8+ years of data engineering experience, strong Python and SQL skills, biotech or pharmaceutical experience, GxP familiarity, and cloud infrastructure experience. | About TetraScience TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. We help the world’s leading life sciences firms turn fragmented scientific data into AI-native assets and scientific workflows that accelerate discovery, development, and manufacturing. TetraScience’s growing ecosystem of strategic partners includes NVIDIA, Databricks, Thermo Fisher Scientific, Snowflake, Google, and Microsoft. In connection with your candidacy, you will be asked to carefully review “The Tetra Way,” authored by our CEO, Patrick Grady; it is impossible to overstate the importance of this document, and you should take it literally as you decide whether our mission, culture, and expectations are right for you. What You Will Do You will join TetraScience's forward deployed teams as a senior, hands-on engineer. In this role, you will work directly with biotech, pharmaceutical, and scientific companies, bringing advanced data and AI technologies into the labs that research, develop, and manufacture medicines. You will work alongside customer scientific, engineering, and IT teams to design, build, and ship integrations and data products on Tetra OS that serve scientific teams across the globe. You will also mentor other scientific data engineers, lead internal design sessions, and partner with Product Managers and Solution Architects to turn customer, product, and engineering objectives into commercialized solutions. Act as a forward deployed engineer for a portfolio of biotech, pharmaceutical, and scientific customers. Own the technical relationship, understand their scientific workflows, and deliver solutions that move their programs forward. Drive AI agents to design and build bidirectional integrations between Tetra OS and customer systems such as ELN, LIMS, MES, and process historians, including serverless push and pull pipelines, stateful connectors, and full stack data applications in Python and TypeScript. Drive AI agents to design and build comprehensive data schemas and parsers for scientific data from R&D lab instruments, manufacturing, CRO, CDMO, ELN, and LIMS sources, across formats such as .xlsx, .pdf, .txt, .raw, .fid, and many other vendor binaries. Deploy and test solutions in customer environments, including cloud, on-premises, and hybrid networks, and troubleshoot across the full stack when something breaks. Work with product managers, solution architects, business analysts, data architects, and ML engineers to understand business requirements, recommend outcomes, and build best-in-class data products based on consensus. Create and enforce designs with quality backed by comprehensive test automation to prove expected behaviors. Lead process and technology improvements supporting product quality, developer experience, and platform capabilities. Rally the team to finish Agile sprint commitments. Actively surface team inefficiencies and resolve them. Drive results. Bring pragmatic urgency to resolving blockers and unclear requirements, and make things happen. Provide mentorship to junior scientific data engineers and show leadership on every front. 8+ years building data products as a Data Engineer or in a similar field. 8+ years working in Python and SQL with a focus on data, integrations, and scientific applications. Expertise in Python and working knowledge of JavaScript, TypeScript, Node, and React. History leading customer-facing implementation projects across cross-functional teams, managing requirements, timelines, and delivery milestones. Experience working with non-clinical data and lab scientists. Familiarity with the biotech and pharmaceutical industry and regulated environments such as GxP. Familiarity with networking, CI/CD, and full stack development. Familiarity with cloud engineering on AWS, including core services such as S3, Lambda, ECS, SQS, SNS, EC2, and CloudWatch. Excellent communication skills, attention to detail, and the confidence to take control of project delivery. Ability to quickly understand a highly technical product and communicate effectively with customers, product management, engineering leadership ​Competitive compensation Stock options in a VC-backed Series C company 100% employer-paid benefits for all eligible employees and immediate family members 401(k) Unlimited paid time off (PTO) Flexible working arrangements Company-paid Life Insurance, LTD/STD The salary range for this position is $140,000 - $200,000 USD Annually. The salary range posted reflects our target baseline for this role. Final compensation is determined by a thorough evaluation of factors including the candidate’s specific experience, localized market data, and internal team equity.

Architect and maintain scalable distributed applications using C#, .NET, and TypeScript with AWS microservices and CI/CD pipelines. | Strong proficiency in C#/.NET, TypeScript/React, AWS cloud services, microservices architecture, production debugging, automated testing, and CI/CD pipelines. | ABOUT TETRASCIENCE TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. We help the world’s leading life sciences firms turn fragmented scientific data into AI-native assets and scientific workflows that accelerate discovery, development, and manufacturing. TetraScience’s growing ecosystem of strategic partners includes NVIDIA, Databricks, Thermo Fisher Scientific, Snowflake, Google, and Microsoft. In connection with your candidacy, you will be asked to carefully review “The Tetra Way,” authored by our CEO, Patrick Grady; it is impossible to overstate the importance of this document, and you should take it literally as you decide whether our mission, culture, and expectations are right for you. The Role We are seeking a highly skilled Senior .NET Engineer to join our dynamic team. The ideal candidate will have extensive experience in designing and developing high-performance, scalable applications using C#, the .NET Framework, and TypeScript. The role involves implementing automated testing and leveraging GitHub Actions for CI/CD pipelines. The candidate should also be proficient in production debugging and possess excellent communication skills. Knowledge and experience with AWS cloud services are also essential. What You Will Do High-Performance, Distributed, and Scalable Application Design: Architect, design, and maintain distributed applications, ensuring high performance, scalability, and security. Full-Stack Development: Develop backend services (primarily C#/.NET and TypeScript) and cloud APIs, as well as modern, responsive front-end applications using TypeScript/React. Microservices Architecture: Design and implement loosely coupled, independently deployable services using AWS services such as DynamoDB, RDS, SQS, Lambda, API Gateway, and others. Automated Testing: Create and maintain automated unit, integration, contract, and end-to-end tests across microservices and UI layers. CI/CD Pipelines: Use GitHub Actions to implement and maintain CI/CD pipelines for both backend services and front-end applications. Production Debugging & Optimization: Diagnose and resolve production issues in distributed systems, including service-to-service communication, CPU/memory/network bottlenecks, and AWS service performance. Observability & Operational Excellence: Instrument services using OpenTelemetry (or equivalent) to enable earlier fault detection, clearer alerting, and stronger operational visibility. Contribute to SLO definition and MTTR reduction efforts across the platform. Collaboration & Communication: Work closely with product managers, tech leads, and other engineers to deliver reliable, maintainable, and scalable solutions. Communicate technical decisions clearly to both technical and non-technical stakeholders. What You Will Bring Required Skills Strong proficiency in backend service development using C#/.NET. Strong proficiency in TypeScript and React for front-end UI. Experience building and operating software in both on-premises/customer-hosted environments and cloud-native multi-tenant environments, with an understanding of how debugging, deployment, and scaling differ between the two. Hands-on experience with AWS DynamoDB, RDS, SQS (or an equivalent cloud provider) in production environments. Experience with microservices patterns (service discovery, API gateway, messaging/queueing, data partitioning). Experience with automated testing at multiple levels. Strong Git skills and hands-on experience with GitHub Actions for CI/CD. Strong troubleshooting skills for distributed systems in production. Solid understanding of cloud-native architecture and AWS best practices. Familiarity with observability tooling and practices (OpenTelemetry, structured logging, distributed tracing, metrics-based alerting) to support proactive issue detection. Desirable Skills Familiarity with containerization (Docker) and experience defining AWS infrastructure using CloudFormation (or AWS CDK) to support predictable and repeatable environments. Experience with performance tuning and designing scalable solutions. Experience with, or exposure to, scientific instrument software / Chromatography Data Systems (CDS) e.g., Waters Empower, Thermo Chromeleon, Cytiva UNICORN, Agilent OpenLab, along with knowledge of instrument integration pattern and practices is a strong plus. Soft Skills Needed: Excellent verbal and written communication. Strong collaboration skills in cross-functional teams. Analytical mindset with high attention to detail. Why TetraScience Mission-driven: your infrastructure directly enables scientific drug development processes that save and extend lives. Builder culture: everyone ships — engineers, managers, and leadership. No passengers. Technically ambitious: we are rebuilding how science-grade data infrastructure works, not deploying off-the-shelf solutions. AI-first: we apply AI across every layer of the platform today, not as a future roadmap item. Competitive compensation, meaningful equity, and strong benefits. 100% employer-paid benefits for all eligible employees and immediate family members Unlimited paid time off (PTO) 401K Flexible working arrangements - Remote work Company paid Life Insurance, LTD/STD A culture of continuous improvement where you can grow your career and get coaching No visa sponsorship is available for this position The salary range for this position is $170,000-$220,000 USD. The salary range posted reflects our target baseline for this role. Final compensation is determined by a thorough evaluation of factors including the candidate’s specific experience, localized market data, and internal team equity.

Architect and maintain scalable distributed applications with CI/CD and observability. | Strong proficiency in C#/.NET, TypeScript, React, AWS, microservices, automated testing, and production debugging. | ABOUT TETRASCIENCE TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. We help the world’s leading life sciences firms turn fragmented scientific data into AI-native assets and scientific workflows that accelerate discovery, development, and manufacturing. TetraScience’s growing ecosystem of strategic partners includes NVIDIA, Databricks, Thermo Fisher Scientific, Snowflake, Google, and Microsoft. The Role We are seeking a highly skilled Senior .NET Engineer to join our dynamic team. The ideal candidate will have extensive experience in designing and developing high-performance, scalable applications using C#, the .NET Framework, and TypeScript. The role involves implementing automated testing and leveraging GitHub Actions for CI/CD pipelines. The candidate should also be proficient in production debugging and possess excellent communication skills. Knowledge and experience with AWS cloud services are also essential. What You Will Do High-Performance, Distributed, and Scalable Application Design: Architect, design, and maintain distributed applications, ensuring high performance, scalability, and security. Full-Stack Development: Develop backend services (primarily C#/.NET and TypeScript) and cloud APIs, as well as modern, responsive front-end applications using TypeScript/React. Microservices Architecture: Design and implement loosely coupled, independently deployable services using AWS services such as DynamoDB, RDS, SQS, Lambda, API Gateway, and others. Automated Testing: Create and maintain automated unit, integration, contract, and end-to-end tests across microservices and UI layers. CI/CD Pipelines: Use GitHub Actions to implement and maintain CI/CD pipelines for both backend services and front-end applications. Production Debugging & Optimization: Diagnose and resolve production issues in distributed systems, including service-to-service communication, CPU/memory/network bottlenecks, and AWS service performance. Observability & Operational Excellence: Instrument services using OpenTelemetry (or equivalent) to enable earlier fault detection, clearer alerting, and stronger operational visibility. Contribute to SLO definition and MTTR reduction efforts across the platform. Collaboration & Communication: Work closely with product managers, tech leads, and other engineers to deliver reliable, maintainable, and scalable solutions. Communicate technical decisions clearly to both technical and non-technical stakeholders. What You Will Bring Required Skills Strong proficiency in backend service development using C#/.NET. Strong proficiency in TypeScript and React for front-end UI. Experience building and operating software in both on-premises/customer-hosted environments and cloud-native multi-tenant environments, with an understanding of how debugging, deployment, and scaling differ between the two. Hands-on experience with AWS DynamoDB, RDS, SQS (or an equivalent cloud provider) in production environments. Experience with microservices patterns (service discovery, API gateway, messaging/queueing, data partitioning). Experience with automated testing at multiple levels. Strong Git skills and hands-on experience with GitHub Actions for CI/CD. Strong troubleshooting skills for distributed systems in production. Solid understanding of cloud-native architecture and AWS best practices. Familiarity with observability tooling and practices (OpenTelemetry, structured logging, distributed tracing, metrics-based alerting) to support proactive issue detection. Desirable Skills Familiarity with containerization (Docker) and experience defining AWS infrastructure using CloudFormation (or AWS CDK) to support predictable and repeatable environments. Experience with performance tuning and designing scalable solutions. Experience with, or exposure to, scientific instrument software / Chromatography Data Systems (CDS) e.g., Waters Empower, Thermo Chromeleon, Cytiva UNICORN, Agilent OpenLab, along with knowledge of instrument integration pattern and practices is a strong plus. Soft Skills Needed: Excellent verbal and written communication. Strong collaboration skills in cross-functional teams. Analytical mindset with high attention to detail. Why TetraScience Mission-driven: your infrastructure directly enables scientific drug development processes that save and extend lives. Builder culture: everyone ships — engineers, managers, and leadership. No passengers. Technically ambitious: we are rebuilding how science-grade data infrastructure works, not deploying off-the-shelf solutions. AI-first: we apply AI across every layer of the platform today, not as a future roadmap item. Competitive compensation, meaningful equity, and strong benefits. 100% employer-paid benefits for all eligible employees and immediate family members Unlimited paid time off (PTO) 401K Flexible working arrangements - Remote work Company paid Life Insurance, LTD/STD A culture of continuous improvement where you can grow your career and get coaching No visa sponsorship is available for this position The salary range for this position is $170,000-$220,000 USD. The salary range posted reflects our target baseline for this role. Final compensation is determined by a thorough evaluation of factors including the candidate’s specific experience, localized market data, and internal team equity.