About the Role

Join a post-training research team at an applied AI data company focused on improving foundation models. You will design controlled experiments to measure how training data influences model capabilities, generalization, and alignment, then turn findings into practical recommendations.

What You'll Do

Run controlled supervised fine-tuning and reinforcement learning experiments to measure the impact of datasets on model performance.

Help develop evaluation benchmarks and new data types that advance model training and assessment.

Build shared evaluation infrastructure to make findings reproducible across models, capabilities, and domains.

Publish external research, technical reports, and blog posts based on your work.

Use experimental results to help improve data quality and production.

Collaborate with external research partners to communicate clear evidence about data-driven improvements and the conditions behind them.

What We're Looking For

Zero to four years of experience building or delivering work in LLM training, post-training, or model evaluation.

Prior experience at a frontier AI lab.

Strong familiarity with LLM training and evaluation methodologies, including SFT and RL-based post-training.

Experience independently designing, implementing, and analyzing controlled experiments, and extracting useful conclusions from imperfect results.

Software engineering skills to build or modify experimental infrastructure, such as evaluation harnesses, training pipelines, or benchmarks.

Ability to work across domains such as finance, software engineering, and policy, with a practical, builder-oriented approach.

Research experience in model evaluation, post-training, or data curation is valuable.

Compensation & Benefits

Base salary: $150,000 to $250,000 annually. Visa sponsorship is available.

Location

On-site in San Francisco, California, United States.