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

About the Job

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)