Software Engineer, RL Environments

About the Role

Design datasets, evaluation rubrics, and reward signals that help improve frontier AI models. You will work with research teams to test data-collection strategies, investigate model failure modes, and deliver evaluations and training data that inform model development.

What You'll Do

• Design data slices that expose meaningful model failure modes across areas such as finance, software, 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 assessing dataset quality, diversity, and downstream impact.

• Create and manage real-world and synthetic data pipelines.

• Translate research objectives into concrete data and evaluation specifications in collaboration with research teams.

What We're Looking For

• One to four years of hands-on experience designing machine learning data-collection strategies and evaluation rubrics.

• Strong familiarity with LLM training and evaluation methods, including experience creating data slices to reveal model failure modes.

• Ability to design lightweight experiments, move quickly, and extract useful insights from messy results.

• Comfort working across domains such as finance, software engineering, and policy.

• A practical, builder-oriented approach and genuine interest in how data structure and quality shape model behavior.

Compensation & Benefits

Base salary: $260,000 to $290,000 annually. Visa sponsorship is available.

Location

On-site in San Francisco, California.