Software Engineer, RL Environments

True; San Francisco (CA); On-site; USD 260,000 - 290,000; Full time

Job summary

On-site in San Francisco, California, our client is seeking a role to build datasets, evaluation rubrics, and reward signals for large language models. You will collaborate with AI research teams to test data-collection approaches, analyze model failure modes, and develop measurements guiding training runs.

The candidate should have 1–4 years of hands-on experience designing data-collection strategies and evaluation rubrics, familiarity with LLM training/evaluation, and the ability to design

Qualifications

• Hands-on experience designing ML data-collection strategies and evaluation rubrics.
• Familiarity with LLM training and evaluation methods.
• Experience creating data slices to reveal model failure modes across domains.

Responsibilities

• Design data slices to 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.

Skills

Data collection design

Evaluation rubrics

Experiment design

Model evaluation methods

Domain familiarity (finance, software,

Education

Bachelor's degree in CS/ML/Statistics

Tools

Python

ML tooling

Job 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.

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.