Requirements

• Have experience building or shipping machine learning or LLM-powered products.
• Strong in Python and comfortable with modern ML tooling.
• Worked on model evaluation, fine-tuning, or prompt design.
• Think in terms of systems and user outcomes, not just model metrics.
• Enjoy debugging messy, real-world failures and turning them into improvements.
• Want to work in the layer that turns research and model potential into systems that actually work for users.

Nice to Haves

• Experience with agent frameworks or tool-using LLM systems.
• Research experience with code generation models or developer tooling.
• Experience working with large, messy datasets or production logs.

What You'll Be Doing

• Design and iterate on agent behaviors across real-world coding tasks and long-horizon workflows.
• Work closely with research to develop and run evals to measure agent performance, regressions, failure modes, and edge cases.
• Improve performance through prompting, tool-use strategies, context construction, and model-facing experimentation.
• Analyze failures in production and systematically improve robustness and reliability.
• Build feedback loops and data systems that get better real-task data into evaluation and research.
• Work with product teams to shape user-facing agent experiences and the interfaces the agent depends on.
• Help define what “good” looks like for agents completing complex tasks end-to-end.

Perks and Benefits

• AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity.
• Equal opportunity employer.
• Reasonable accommodations for applicants with disabilities.