Post-Training Research Engineer: RLHF, Evaluation & Stats

present of AI; San Francisco (CA); In loco; USD 180.000 - 280.000; Tempo pieno

Descrizione del lavoro

Cognition in San Francisco is seeking a leader for the post-training loop of coding agents, responsible for building training recipes, designing evals, and diagnosing why results look strange.

The role requires depth in preference learning and reward methods such as RLHF, plus solid statistics to tell true effects from noise. The ideal candidate has trained large models across many GPUs and has published or open-source ML work. Priorities may shift quickly, demanding clear judgment.

Competenze

• Experience with RLHF or preference modeling projects
• Statistical rigor on experiments
• Published or open-source ML work

Mansioni

• The role covers the whole post-training loop for coding agents: building training recipes, designing evals, and working out why results look strange.
• This job is the AI work itself, since the person shapes how an autonomous software agent learns, behaves and gets measured.
• Someone in this role would work on training and aligning an agent that plans, writes, debugs and deploys code, using very large compute.

Descrizione del lavoro

Cognition in San Francisco is seeking a leader for the post-training loop of coding agents, responsible for building training recipes, designing evals, and diagnosing why results look strange.

The role requires depth in preference learning and reward methods such as RLHF, plus solid statistics to tell true effects from noise. The ideal candidate has trained large models across many GPUs and has published or open-source ML work. Priorities may shift quickly, demanding clear judgment.