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 3,000 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
7,000+ customers
Raised $30M from Benchmark, YC, Paul Graham, Initialized
Team
We have assembled a small, talent dense team who have scaled critical functions at companies like Stripe, Google, Figma, LinkedIn, etc.
Qualifications
BS in Computer Science or equivalent
Some research experience, ideally with ML/LMs/Agents
Strong programming skills and good product intuition
Responsibilities
Experiment with and apply recent advancements in agents/LMs etc. to improve the performance and capabilities of our products
Example: you might study multi-agent architectures, prototype and evaluate a multi-agent code review workflow, and then work with a team to integrate successful prototypes into production systems
Stay current with the latest research in LLMs, information retrieval, and developer tooling