Research Scientist, Post-Training (AI Data Startup)

A venture-backed startup supplies datasets for model training, plus evaluation tooling, to leading AI labs. It is a small team where individual researchers directly influence how future models learn. The scientist in this seat will show, through experiments, that the data really changes model behavior.

Day to day

Plan and execute controlled supervised fine-tuning and reinforcement-learning experiments that isolate the effect of specific datasets

Measure gains in areas such as reasoning, tool use, extended multi-step tasks and workflows tied to specific domains

Present results to partner labs in a form that backs up the commercial conversation

Collaborate with internal teammates to refine data quality using what the experiments reveal

Research profile

Two to six years of post-training work on large models, including hands-on reinforcement-learning methods

Solid grounding in LLM training and evaluation, plus strong PyTorch and distributed-training skills

Fascination with how the structure, selection and quality of data shape model behavior

Able to design lean experiments quickly and draw useful conclusions from noisy outcomes

Comfortable moving between domains such as finance, software engineering and policy

Prefers building to theorizing; a PhD is not required

On-site in the San Francisco Bay Area. Visa transfers are considered.