- You have personally built and shipped a substantial agentic system. Production use or rigorous, reproducible open-source work matters more than the name of a framework or employer.
- You have deep practical experience with LLM tool use, planning, context engineering, and evaluations. You have implemented multi-agent coordination or substantial parallel agent/tool execution and can explain its failure modes.
- You have hands-on experience with browser or computer automation in an agentic system, including observing state, verifying effects, and recovering when an interface or execution path fails.
- You are an excellent software engineer in Python, TypeScript, or a comparable language. You are comfortable with asynchronous services, state machines, persistence, concurrency, retries, and cancellation.
- You know which decisions belong to a model and which guarantees must be enforced in code. You can reason carefully about permissions, untrusted inputs, uncertain external outcomes, and human approvals.
- You can design meaningful experiments, debug real system behavior, and explain what improved, why it improved, and where the evidence is still weak.
- You can take technical ownership of an unclear problem, work effectively with other engineers, and ship with urgency and care.
Useful additional experience
Depth in agent memory and retrieval, skill acquisition, reinforcement learning or post-training, trajectory datasets, sandboxed execution, distributed systems, inference optimization, or multimodal and voice models would be valuable. We expect strong foundations and particular depth in a few areas, rather than prior specialization in every one.
There is no required degree, publication record, previous employer, or agent framework. We’re hiring for demonstrated engineering ability, judgment, and ownership.
How we work
This role is based in our San Francisco office. Expect a small team, short feedback cycles, direct communication, and high standards. We value people who move quickly, take responsibility for the result, surface problems early, and change their minds when the evidence calls for it.
You’ll have substantial freedom to explore ambitious technical ideas and the responsibility to turn the best ones into software that works. We’ll discuss the project in more detail during the interview process.
Interview process
Our conversations will center on systems you’ve built, a practical agent architecture and debugging exercise, and how you work with a team.
When applying, tell us about one agentic system you personally owned: what you built, the hardest failure you fixed, and how you measured the improvement. A project link, technical write-up, or open-source contribution is welcome. You can discuss confidential work without sharing proprietary material.
The base salary range is $250,000–$325,000 USD annually, plus equity. The final offer will reflect relevant experience, demonstrated skills, and the scope of responsibility.