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Pinecone is seeking an Applied AI Engineer to build end-to-end AI applications using its vector database and knowledge infrastructure. This is a hands-on, full-stack role where you'll design, develop, and ship production AI applications spanning frontend interfaces, backend services, APIs, data pipelines, and LLM integration.
You'll work closely with engineering, product, customers, and partners to explore new use cases, build reference applications, and demonstrate what's possible with Pinecone's technology. Key responsibilities include:
- Design and build end-to-end AI applications using Pinecone's retrieval and knowledge infrastructure
- Develop full-stack solutions across frontend, backend, APIs, data pipelines, and LLM integration
- Build reference applications, integrations, and reusable components to help developers and customers adopt Pinecone
- Experiment with emerging AI patterns including RAG, agents, tool use, context engineering, and agent memory
- Work with customers and partners to understand real-world problems and translate them into working software
- Evaluate and improve quality, reliability, latency, and cost of AI-powered applications
- Share technical insights and best practices through code, examples, and technical content
- Bring learnings from application building back into Pinecone's product and engineering teams
This role combines strong full-stack engineering with practical LLM and agentic systems experience. You should be comfortable discussing architecture, writing production code, and building working prototypes. The ideal candidate enjoys moving quickly, experimenting with emerging technologies, and owning projects from initial idea to production.
REQUIREMENTS:
- 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 (React, Next.js)
- Experience building APIs, integrating external services, and working with databases and cloud infrastructure
- Ability to independently take projects from rough requirements through implementation and deployment
- 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 practical challenges in building AI applications: retrieval quality, evaluation, latency, reliability, and cost
- Curiosity about emerging patterns in agentic applications, context management, and AI-driven workflows
- Builder's mindset with comfort working with ambiguous requirements and making pragmatic technical decisions
- Ability to communicate technical ideas clearly to engineers, customers, and stakeholders
- Comfortable moving between rapid prototypes and production-quality implementations
BONUS:
- Experience with Pinecone or other vector databases and search technologies
- Experience building agentic applications, agent memory systems, or complex RAG pipelines
- Open-source contributions or involvement in developer tool communities
- Experience working directly with customers on technical projects
- Experience deploying and operating AI applications in production
About Pinecone
AI / Data / Infrastructure — vector database infrastructure for AI applications and semantic search.
