Pinecone

You would build end-to-end AI applications using Pinecone infrastructure and work with teams and customers to turn real-world problems into scalable software. | The role requires 5–10 years of software engineering experience, production application development with Python or TypeScript, and hands-on LLM, vector database, and modern AI framework experience. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. About the Team and Role We're looking for an Applied AI Engineer who loves building real applications with AI. You'll work across the stack, from backend services and retrieval pipelines to APIs and user-facing applications, turning new AI capabilities into working solutions. This is a hands-on engineering role for someone who enjoys moving quickly, experimenting with emerging technologies, and owning projects from initial idea to production. You'll work closely with our engineering and product teams, as well as customers and partners, to explore new use cases, build reference applications, and demonstrate what's possible with Pinecone's technology. The ideal candidate combines strong full-stack engineering skills with practical experience building LLM-powered applications and agentic systems. You should be equally comfortable discussing architecture, writing production code, and putting together a working prototype. Responsibilities Design, build, and ship end-to-end AI applications using Pinecone’s retrieval and knowledge infrastructure. Develop full-stack solutions spanning frontend interfaces, backend services, APIs, data pipelines, and LLM integration. Build reference applications, integrations, and reusable components that help developers and customers adopt Pinecone. Experiment with emerging AI patterns, including RAG, agents, tool use, context engineering, and agent memory. Work with customers, partners, and internal teams to understand real-world problems and translate them into working software. Evaluate and improve the quality, reliability, latency, and cost of AI-powered applications. Share technical insights and best practices through code, examples, and technical content. Bring learnings from building applications back into Pinecone’s product and engineering teams. What You’ll Bring to the Table You don't need to check every box, but you should enjoy building software and be excited about what AI makes possible. Full-Stack Engineering Strong software engineering background with experience designing, building, and shipping production applications. Proficiency in Python and/or TypeScript, including modern backend frameworks and frontend technologies such as React or Next.js. Experience building APIs, integrating external services, and working with databases and cloud infrastructure. Ability to independently take a project from rough requirements through implementation and deployment. Applied AI Hands-on experience building applications using LLMs, embeddings, vector databases, or retrieval-augmented generation. Familiarity with modern AI development tools, frameworks, and APIs. Understanding of the practical challenges of building AI applications, including retrieval quality, evaluation, latency, reliability, and cost. Curiosity about emerging patterns in agentic applications, context management, and AI-driven workflows. Ownership & Collaboration A builder's mindset: you enjoy writing code, experimenting, and getting working software into users’ hands. Comfortable working with ambiguous requirements and making pragmatic technical decisions. Ability to communicate technical ideas clearly to engineers, customers, and other stakeholders. Comfortable moving between rapid prototypes and production-quality implementations. Interest in understanding how developers and customers use the technology and translating that feedback into better solutions. Bonus Points Experience with Pinecone or other vector databases and search technologies. Experience building agentic applications, agent memory systems, or complex RAG pipelines. Contributions to open-source projects, developer tools, or technical communities. Experience working directly with customers on technical projects. Experience deploying and operating AI applications in production. Perks & Benefits Comprehensive health coverage including medical, dental, vision, and mental health resources 401(k) Plan Equity award Flexible time off Paid parental leave Annual Company Retreat WFH Equipment Stipend Benefits and eligibility may vary by country of employment.

Build and optimize scalable search and retrieval infrastructure, including indexing pipelines, backend APIs, and systems for reliability, performance, and retrieval quality. | Requires deep production backend and distributed-systems experience, expertise in a major systems language, and direct experience or strong theoretical knowledge of semantic search, vector databases, hybrid retrieval, and RAG. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Build and guide scalable search and retrieval infrastructure, including indexing pipelines, backend services, APIs, and systems for reliability, performance, and retrieval quality. | Requires senior-level production backend architecture, data engineering and indexing expertise, direct or theoretical knowledge of semantic or hybrid search and RAG, and fluency in a major backend language. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Design and build scalable backend components for AI-powered search and retrieval infrastructure. | Senior-level backend engineering with expertise in large-scale system architecture, data indexing pipelines, semantic search, and infrastructure tools like Kubernetes and Terraform. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Build scalable search and retrieval infrastructure, indexing pipelines, and APIs while improving retrieval quality, performance, reliability, and security. | Requires senior-level production backend and distributed-systems experience, data engineering, direct or theoretical search and retrieval knowledge, strong proficiency in a major systems language, and familiarity with modern infrastructure. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Design and build scalable backend components and indexing pipelines for AI-powered semantic search and retrieval infrastructure. | 6+ years shipping production-grade backends, expertise in system architecture, data engineering, semantic search or vector DBs, proficiency in languages like Go, Rust, C++, Java, or Python, and familiarity with cloud-native infrastructure and observability. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Design and build scalable backend components for AI-powered search and retrieval infrastructure. | 6+ years backend experience, expertise in scalable systems, semantic search or vector DB knowledge, proficiency in Go, Rust, C++, Java, or Python, and familiarity with cloud infrastructure and observability. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Build and guide scalable search and retrieval infrastructure, including indexing pipelines, backend services, APIs, and reliability and performance improvements. | Requires senior production-backend architecture experience, data engineering and indexing expertise, direct or theoretical search and retrieval knowledge, RAG familiarity, and fluency in a major systems language. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Design and build scalable backend components for AI-powered search and retrieval infrastructure. | Senior-level experience in backend systems, data engineering, semantic search or vector databases, proficiency in major programming languages like Go, Rust, C++, Java, or Python, and familiarity with cloud infrastructure and observability. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Design and build scalable backend systems for AI-powered knowledge retrieval infrastructure. | Senior-level backend system architecture experience with knowledge of semantic search, vector DBs, and infrastructure tools like Kubernetes and Terraform. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Design and build scalable search and retrieval infrastructure, indexing pipelines, backend services, and APIs while improving retrieval quality, performance, reliability, and security. | Requires senior-level production backend and distributed-systems experience, data engineering and search or retrieval expertise, fluency in a major systems language, and familiarity with cloud infrastructure and observability. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Design and build scalable backend components for AI-powered search and retrieval infrastructure. | 6+ years backend production experience, expertise in system architecture, data engineering, semantic search or vector DBs, and proficiency in languages like Go, Rust, C++, Java, or Python. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Design and build scalable backend systems and APIs for AI-powered search and retrieval infrastructure. | Senior-level experience with backend system architecture, distributed systems, and familiarity with AI retrieval techniques and modern infrastructure tools. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Design and build scalable backend components for AI-powered search and retrieval infrastructure. | Senior-level experience in backend system architecture, distributed systems, and AI infrastructure with proficiency in major programming languages and cloud infrastructure tools. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Design and build scalable backend components for AI-powered search and retrieval infrastructure with ownership of architecture, performance, and reliability. | Senior-level experience designing production-grade backend systems, data engineering pipelines, semantic search or vector databases, and fluency in major programming languages with infrastructure and observability expertise. | About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io. About The Team And Role We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities • Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security What You’ll Bring To The Table To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise • Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. • Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval • Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical • Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. • Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact • Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. • Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points • Experience building multi-tenant SaaS platforms. • Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. • Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits • Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws. Compensation Range: $190K - $270K

Design and build end-to-end AI agent experiences including APIs, SDKs, and observability systems. | At least 4 years software engineering experience with API design, React, and AI coding tools. | About Pinecone Pinecone is the knowledge infrastructure for AI at scale. Its leading vector database and knowledge engine, Pinecone Nexus, power accurate, performant AI applications for more than 9,000 customers and 800,000 developers worldwide. Pinecone's mission is to make AI knowledgeable. Pinecone is based in New York and raised $138M in funding from Andreessen Horowitz, ICONIQ, Menlo Ventures, and Wing Venture Capital. About the Team and Role: The Experience team is at the center of one of the most exciting transitions in software development history — the shift from human-driven to agent-driven product experiences. We own Pinecone's API, clients, authentication, revenue, and observability systems, and right now that means redesigning all of it for a world where AI agents are first-class users alongside humans. This is a wide-scope role. You'll own things end-to-end — from backend architecture to API design to SDK and web surfaces. You’ll be working closely with product, design, and other engineering teams to identify user needs and build the right thing, at the right abstraction level, at the right time. Along the way, you will be building high-leverage platform capabilities that accelerate Pinecone’s product development and user growth systems. We're looking for an engineer who sees this moment for what it is: a rare opportunity to shape how developers and agents interact with a category-defining product. You're not waiting to see how the industry figures out MCP, agentic workflows, and AI-native interfaces — you're already experimenting, already forming opinions, already building. You know that speed and leverage matter more than labor, and you've internalized AI-assisted development not as a productivity trick but as a fundamentally different way of working. Responsibilities: Pioneer our agent experience. Shape how AI agents interact with Pinecone — designing interfaces, protocols (MCP), and tooling that make Pinecone the easiest and most capable platform for agentic workflows to build on. Ship end-to-end experiences across API, SDK, CLI, and web. You'll own the full surface, from the HTTP layer to the UI, and you'll make calls across all of it. Build systems that multiply team output. We care about what ships, not how many hours went into it. Design AI-powered workflows, automated quality gates, and high-leverage platform capabilities that let a small team move at a pace that would otherwise require 10x the headcount. Set the standard for AI-assisted development on the team. Use the best tools available — Claude Code, Cursor, or whatever works — and help establish patterns that make the whole team faster. Collaborate with product, design, and engineering to identify what users (and agents) actually need and build it at the right abstraction level, at the right time. Build observable, auditable systems with monitoring, alerting, and verification mechanisms — because in a high-velocity environment, you need systems that tell you when something's wrong. Requirements: Senior and Staff Engineers at Pinecone operate with high autonomy. You'll be an active partner in setting strategy on APIs, agent interfaces, observability, and developer experience — not just executing on a roadmap. We care more about how you think and how you work than whether your resume checks every box. You should have: At least 4 years of software engineering experience A bias toward automation and leverage — you reach for systems and tooling before you reach for elbow grease Hands-on experience shipping with AI coding tools (Claude Code, Cursor, Copilot, or similar) as a genuine part of your workflow Proven ability to design and deliver versioned APIs, SDKs, or client libraries Proficiency building web interfaces with React or similar Experience with at least one major cloud provider (AWS, GCP, Azure) Strong intuition for what makes APIs easy for both humans and agents to use correctly Bonus points for: Experience building or consuming MCP servers, or designing APIs explicitly for agent use cases Familiarity with agentic frameworks and the emerging patterns around tool use, orchestration, and agent-to-agent communication Strong familiarity with a compiled language like Rust, Go, or Java Experience with large-scale open-source projects Experience developing database or query engine SDKs Perks & Benefits: Comprehensive health coverage including medical, dental, vision, and mental health resources 401(k) Plan Equity award Flexible time off Paid parental leave Annual Company Retreat WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws.

Design and build scalable platform components and backend services for advanced retrieval and AI systems. | 6+ years shipping production-grade backends with expertise in retrieval strategies and proficiency in languages like Go, Rust, or Python. | About Pinecone Pinecone is the knowledge infrastructure for AI at scale. Its leading vector database and knowledge engine, Pinecone Nexus, power accurate, performant AI applications for more than 9,000 customers and 800,000 developers worldwide. Pinecone's mission is to make AI knowledgeable. Pinecone is based in New York and raised $138M in funding from Andreessen Horowitz, ICONIQ, Menlo Ventures, and Wing Venture Capital. About the Team and Role: We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities: Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation Design and build optimized indexing pipelines for structured and unstructured data Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration Improve retrieval quality through evaluation and observability frameworks Design APIs for internal and external user and agentic consumers Optimize latency, throughput and cost across large-scale inference and retrieval workloads Drive technical direction for reliability and security What You’ll Bring to the Table: To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. Technical Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. Ownership & Impact Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points Experience building multi-tenant SaaS platforms. Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results. Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits: Comprehensive health coverage including medical, dental, vision, and mental health resources 401(k) Plan Equity award Flexible time off Paid parental leave Annual Company Retreat WFH Equipment Stipend All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws.