Software Engineer, RL Environments

Description

About the Role

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.

Responsibilities

• 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.

Requirements

• 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.

Conditions

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

On-site in San Francisco, California.