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Post-Training Research Scientist
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Baseten·San Francisco·17 мар.

Post-Training Research Scientist

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Наша компания

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 $1.5B Series F led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products.

Чем предстоит заниматься

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
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

Наши требования

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 engineers who have spent years inside a production stack and developed insights no PhD program teaches; PhDs working on the wrong-shaped problem at the right-shaped lab; operators who have been close to real systems long enough to see things credentialed researchers have never had to see. If you don’t have the traditional qualifications and are doing exceptional work, we’d love to chat

Мы предлагаем

Competitive compensation, including meaningful equity
100% coverage of medical, dental, and vision insurance for employee and dependents
Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
Paid parental leave
Fertility and family-building stipend through Carrot
Company-facilitated 401(k)
Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities

Дополнительно

Towards infinite context windows: neural KV cache compaction
Repeated kv cache for long-running agents
Distillation without the dark – replicating black-box on-policy distillation on Baseten
B
Baseten
San Francisco

ГрейдMiddle
ЗанятостьПолная занятость
РегионСША
ФорматОфис
ИсточникСкрыто
Опубликовано17 мар.
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