About the role
We want to build agents that autonomously validate code changes. Today that looks like AI that reviews pull requests in GitHub, catching bugs and enforcing standards. We’re reviewing close to 1B lines of code a month now for over 1,000 companies.
Problems we’re excited about
- Coding standards can be idiosyncratic and are often poorly documented; can we build agents that learn them through osmosis like a new hire might?
- Can we identify for each customer what types of PR feedback they do and don’t care about, perhaps using some sample efficient RL, in order to increase signal-to-noise ratio?
- Some bugs are best caught by running the code, potentially against discerning AI-generated E2E tests. Can we autonomously deploy feature branches and use agents to parallel try to break the application to detect bugs?
Trajectory
- Went from 0 ---> XM in 25% MoM
- 1,500+ customers
- Raised 30M+ led by Benchmark, along with continued support from YC, Paul Graham, Initialized, SV Angel, etc..
Team
- We have assembled a small, talent dense team who have scaled critical functions at companies like Stripe, Google, Figma, LinkedIn, etc.
Responsibilities (in order of how much time you’ll likely spend on each)
- Solve some of our hardest and most interesting problems, including LLM memory, multi-language codebase indexing, semantic search for large codebases, and much more.
- Design, implement, test, and deploy full features
- Get user feedback to iterate the features
