Measure and improve quality: Use tests, traces and observability, user feedback, and appropriate LLM or agent evaluations to diagnose failures and improve application behaviour.
Enforcing coding standards and conducting thorough code reviews to maintain high-quality codebase. Implement automated testing frameworks to catch bugs early and ensure software reliability.
The next leap in AI isn't about getting smarter — it's about becoming yours. Seventy years of AI research keep proving the same thing: general methods win over clever designs. Models are getting stronger. Protocols are converging. Infrastructure is commoditizing. But one layer remains scarce — the layer that makes AI truly yours.
Use AI-assisted coding tools (Claude Code, GitHub Copilot, etc.) as a core part of your daily workflow — across design, implementation, testing, debugging, and documentation.
We're looking top tier software engineers as part of a high-leverage team that ships across the entire search and retrieval stack: crawling, parsing, ML systems performance, retrieval algorithms, and new product surfaces. Past output from this team includes shipping entirely new product lines from scratch, cutting ML inference latency multiple times over, and making core infrastructure several times faster. You'll move between problem areas as the business needs it, with real end-to-end ownership from day one.