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. A meaningful portion of your time will be dedicated to research that deepens our understanding of how models learn, alignment, and architectural efficiency — questions that may not have immediate product application. The remainder will be directed toward research that solves concrete problems for Baseten's platform and customers, who are the fastest growing AI companies in the world like Cursor, Lovable, and Notion. 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 - Towards infinite context windows: neural KV cache compaction https://www.baseten.co/research/towards-infinite-context-windows-neural-kv-cache-compaction/ - 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 foundational 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, trillion parameter models). - Work with customers to translate domain-specific requirements into research problems, where relevant to your agenda. - 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 inference production systems. - Mentor junior researchers and shape the technical direction of the research organization as it grows. PREFERRED QUALIFICATIONS - Master’s or PhD 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, the ability to distinguish research that advances a metric from research that changes how systems are built - Willingness to operate in a startup environment where the majority of research informs product decisions, with timelines measured in months rather than years - Background spanning multiple research areas (e.g., both interpretability and RL, or both systems and training methodology) - Track record of open-source contributions or community building in ML research OUR VIEW ON TALENT Many of the labs that exist today run a credentialist talent model. Concentrate the most already-legible researchers, and assume the concentration compounds. The best researchers in this field are very often not yet legible. Research e