RL Engineer

Join a small early-stage team building training data and evaluation infrastructure for advanced AI models. You will design reinforcement learning environments datasets and evaluation methods that help models improve across complex real-world tasks.

• Design data slices and agent tasks that reveal meaningful model failure modes across areas such as finance coding and enterprise workflows.
• Build and refine evaluation rubrics and reward signals for RLHF and RLVR training pipelines.
• Model annotator behavior and run experiments to improve model capabilities.
• Develop quantitative methods for measuring dataset quality diversity and impact on model alignment and capabilities.
• Create and manage real-world and synthetic data pipelines.
• Work with research teams to translate training objectives into concrete data and evaluation specifications.

• One to eight years of experience as a machine learning or software engineer.
• Experience building reinforcement learning environments or strong research experience developing agent tasks.
• Strong backend or full-stack engineering skills in Python TypeScript or similar languages.
• A computer science degree from a highly regarded institution or an exceptional record of relevant experience.
• Experience measuring dataset quality and diversity with AI research publications or relevant side projects as valuable signals.
• A bias for action strong execution and willingness to take on challenging detailed work in a fast-moving team.

Salary range: $260000 to $290000 annually. Visa sponsorship is not available.

Remote with San Francisco California as the primary location.