As an AI Engineer focused on reinforcement learning and evaluations, you will build the systems that help models improve on tasks where there is no single objectively correct answer. This is a production engineering role at the intersection of backend development and applied machine learning, with ownership from environment design through shipped infrastructure.

What you'll do

- Build and scale reinforcement learning environments and evaluation frameworks for creative domains such as design.

- Design tasks and grading rubrics that measure quality in subjective domains.

- Develop agent harnesses and context layers for API and product infrastructure.

- Work with research teams to create environments that improve AI model output.

- Ship production software end to end, including backend systems and data pipelines.

What we're looking for

- At least two years of relevant engineering or applied machine learning experience.

- Experience building reinforcement learning environments, evaluation systems, or machine learning post-training systems.

- Strong backend or full-stack engineering experience shipping production products.

- Practical skills in Python and PyTorch, with working knowledge of reinforcement learning, LLMs, evaluation frameworks, and distributed systems.

- A computer science degree.

- Ability to collaborate across engineering and research and communicate fluently in English.

Bonus points

- Experience building evaluations for creative, generative, or otherwise non-verifiable domains.

- Experience developing platforms or environments for agentic systems.

Compensation and benefits

The base salary range is $175,000 to $275,000 USD, plus competitive equity.

Location / work model

This is a full-time, on-site role in San Francisco, five days per week.