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

Requirements:

• 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)