Software Engineer, RL Environments
True Talent Solutions Partners
About the role
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.
What You'll Do
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.
What We're Looking For
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.
Compensation & Benefits
Base salary is $260,000 to $290,000 annually. Visa sponsorship is available.
Location
On-site in San Francisco, California.
Responsibilities
- Design data slices that expose model failure modes across domains 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 methods to measure dataset quality, diversity, and downstream impact.
- Create and manage real-world and synthetic data pipelines.
- Translate training objectives into concrete data and evaluation specifications with research teams.
Qualifications
- 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 finance, code, or enterprise workflows.
- Ability to design lightweight experiments, interpret ambiguous results, and draw actionable insights.
- Practical, builder-oriented approach and interest in how data quality and structure shape model behavior.
Benefits
- Visa sponsorship available.