Post-Training Research Scientist

ABOUT BASETEN

Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $300M Series E https://www.baseten.co/blog/announcing-baseten-s-300m-series-e/, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Join us and help build the platform engineers turn to to ship AI products.

THE ROLE

This role sits at the frontier of our research agenda. You will pursue open problems at the intersection of post-training methodology and performant inference and then collaborate with research engineering to translate findings into production systems. Roughly a third of your time will be dedicated to pure research: questions that may not have immediate product application but deepen our understanding of models ability to learn, alignment, or architectural efficiency. The remainder will be directed toward research that solves concrete training problems for Baseten's platform and customers which are the fastest growing AI companies in the world like Cursor, Lovable, Notion etc.

We are looking for someone with sharp research taste and genuine creative instinct for problem selection. Someone who can identify questions that matter, design clean experiments to answer them, and push the state of the art. The environment here is not theoretical, but rather research that can be validated with eager customers who are serving billions of tokens a second.

RECENT RESEARCH

- Dense, on-policy or both? https://www.baseten.co/research/dense-on-policy-or-both/

- Repeated kv cache for long-running agents https://www.baseten.co/research/repeated-kv-cache-for-long-running-agents/

- Distillation without the dark – replicating black-box on-policy distillation on Baseten https://www.baseten.co/research/distillation-without-the-dark/

RESPONSIBILITIES

- Define and pursue a research agenda spanning both pure and applied work, with the applied component connected to Baseten's platform and customer needs

- Design and execute rigorous experiments, frequently at meaningful scale (multi-node, 1T+ parameter models)

- Publish at top venues (NeurIPS, ICML, ICLR) and establish Baseten's research presence

- Collaborate with model performance and training infrastructure teams to bridge research findings and production systems

- Mentor junior researchers and shape the technical direction of the research organization as it grows

QUALIFICATIONS

- PhD or equivalent research depth in machine learning, with first-author publications at top venues

- Demonstrated ability to move from theory through implementation to empirical results — not exclusively theoretical or exclusively engineering work

- Judgment about problem selection