Research Scientist, Post-Training

ABOUT THE ROLE Join a research team at an applied AI research and training data company, focused on understanding how datasets affect model behavior. You will design controlled post-training experiments and turn their results into evidence that informs data development and model improvement. WHAT YOU'LL DO - Run controlled supervised fine-tuning and reinforcement learning experiments to measure how datasets affect model performance. - Build shared evaluation infrastructure and benchmarks that support reproducible findings across models, capabilities, and domains. - Help develop public evaluations and new data types that advance model capabilities. - Publish external-facing research, blog posts, and technical reports. - Use experimental results to guide improvements to data quality in collaboration with internal teams. - Work with partner research labs to communicate clear findings about data and model performance. WHAT WE'RE LOOKING FOR - 0 to 4 years of experience building or delivering work in LLM training, post-training, or model evaluation, including experience at a frontier AI lab. - Familiarity with LLM training and evaluation methods, including SFT and RL-based post-training. - Experience independently designing, implementing, and analyzing controlled experiments, and drawing actionable insights from imperfect results. - Software engineering ability 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, hands-on approach. - Research experience involving 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.