Software Engineer, RL Environments

Build datasets, evaluation rubrics, and reward signals that help improve large language models. You will work with AI research teams to test data-collection approaches, investigate model failure modes, and develop measurements that inform training runs.

• Design data slices that reveal 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 pipelines.

• Model annotator behavior and run experiments to improve model capabilities.

• Develop quantitative methods to measure dataset quality, diversity, and downstream impact.

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

• Work with research teams to turn training objectives into concrete data and evaluation specifications.

On-site in San Francisco, California.

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

• Familiarity with large language model training and evaluation methods.

• Experience creating data slices to reveal model failure modes across domains such as finance, code, or enterprise workflows.

• Ability to design lightweight experiments, work through ambiguous results, and draw actionable insights.

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

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