Senior Software Engineer - AI Infrastructure, Developer Tooling

About the Team We're building the AI-powered backbone of our developer tools. You'll work on three core systems: a retrieval infrastructure powering our AI products, a coding agent that assists users in writing code, and the evaluation frameworks that measure their effectiveness. Responsibilities Retrieval Systems (RAG Infrastructure) - Design and scale retrieval pipelines including vector search, BM25, and hybrid retrieval strategies - Build and optimize embedding pipelines, chunking strategies, and re-ranking systems - Develop query understanding and rewriting components to improve retrieval relevance - Manage vector database infrastructure at production scale Coding Agent Development - Build agent architectures that support multi-step code generation, refactoring, debugging, and diagnostics - Implement tool-use patterns (function calling, code execution sandboxing, file system interaction) - Develop context management strategies for long-form code understanding Evaluation & Benchmarking - Design and maintain evaluation frameworks for retrieval quality and agent task completion - Build custom benchmark suites for real-world coding task assessment - Create reproducible testing infrastructure with automated regression detection Minimum Qualifications - Bachelor's degree in CS, EE, or related field (or equivalent experience) - 4+ years of software engineering experience - Strong proficiency in Python (primary) and TypeScript/JavaScript - Experience with at least one systems-level language (C++, Rust, Go) - Practical experience integrating LLMs into applications (prompt engineering, context management, output parsing) - Understanding of agent patterns: tool use, multi-turn reasoning, error recovery - Familiarity with code-specific LLM tasks (generation, summarization, analysis) Preferred Qualifications - Master's or Ph.D. in Computer Science, Machine Learning, or related field - Contributions to open-source developer tooling, retrieval systems, or coding assistants - Experience with AST parsing, code analysis tools, or language servers - Familiarity with rendering pipelines or cross-platform framework architecture - Experience deploying and optimizing ML models in production (latency, cost, reliability)