Research Scientist Frontier Data San Francisco, CA 150K-250K

Location: San Francisco CA (in-person)

Compensation: $150000 - $250000 base plus bonus and equity (total cash compensation can reach $250000 - $450000)

Join a fast-growing AI infrastructure company as a Research Scientist designing the datasets and evaluation frameworks that shape how frontier AI models are trained and measured.

What Youll Do

- Design data slices and explore data shapes that expose meaningful model failure modes across domains including finance code and enterprise workflows

- Build and refine evaluation rubrics and reward signals for RLHF and RLVR training pipelines

- Model annotator behavior and run experiments to improve different model capabilities

- Develop quantitative frameworks for measuring dataset quality diversity and downstream impact on model alignment and capability

- Partner with research teams at the worlds top AI labs to translate their training objectives into concrete data and evaluation specifications

- Move fast from hypothesis to experiment extract actionable insights from messy results and iterate quickly

What Youll Bring

- Strong quantitative instincts with familiarity with LLM training pipelines RLHF or RLVR or evaluation methodology no PhD required

- A genuine intrinsic obsession with how data structure selection and quality drive model behavior

- The ability to design lightweight experiments move fast and extract insights from messy or incomplete results

- Comfort working across domains such as finance software engineering and policy with the ability to context-switch and reason clearly

- A strong bias toward building and shipping experiments over theorizing

Nice to Have

- Prior work or internship at an RL environment company AI safety organization or benchmarking organization

- Background in evaluation methodology benchmark design or dataset curation at a lab or research organization

- Exposure to annotator modeling reward signal design or alignment-related research

This is a high-leverage research seat where your work directly shapes how the next generation of frontier models learns with outsized impact on a small high-caliber team.