训练后研究科学家
Post-Training Research Scientist
关于 BASETEN
Baseten 为全球最具活力的 AI 公司提供关键的推理支持,包括 Cursor、Notion、OpenEvidence、Abridge、Clay、Gamma 和 Writer。通过结合应用 AI 研究、灵活的基础架构和无缝的开发者工具,我们使处于 AI 前沿的公司能够将前沿模型投入生产。我们正在快速成长,并最近完成了 15 亿美元的 F 轮融资 https://www.baseten.co/blog/announcing-our-series-f/,由 Altimeter Capital、Conviction Partners 和 Spark Capital 领投。加入我们,帮助构建工程师们用来部署 AI 产品的平台。
职位描述
该职位位于我们研究议程的最前沿。你将探索后训练方法与高性能推理交叉领域的开放性问题,并与研究工程团队合作,将研究成果转化为生产系统。
你将花费大量时间进行研究,以加深对模型学习、对齐和架构效率的理解——这些问题可能没有直接的产品应用场景。其余时间将用于解决 Baseten 平台和客户(如 Cursor、Lovable 和 Notion 这些世界上增长最快的 AI 公司)面临的具体问题。
我们寻找一位具有敏锐研究品味和真正创造性的解决问题能力的人。能够识别重要问题,设计清晰的实验来解答它们,并推动技术前沿。这里的环境并非纯理论,而是可以被渴望验证的客户所验证的研究,这些客户每秒处理数十亿个 token。
宣言:https://labs.baseten.co/manifesto
近期研究
- 向无限上下文窗口迈进:神经 KV 缓存压缩 https://www.baseten.co/research/towards-infinite-context-windows-neural-kv-cache-compaction/
- 密集、策略性或两者兼具?https://www.baseten.co/research/dense-on-policy-or-both/
- 长期运行代理的重复 KV 缓存 https://www.baseten.co/research/repeated-kv-cache-for-long-running-agents/
- 没有黑暗的蒸馏——在 Baseten 上复制黑盒策略性蒸馏 https://www.baseten.co/research/distillation-without-the-dark/
职责
- 制定并推进涵盖基础研究和应用工作的研究议程,其中应用部分需与 Baseten 平台和客户需求相关联。
- 设计并执行严谨的实验,
查看英文原文
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 $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn 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.
The Manifesto: https://labs.baseten.co/manifesto
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 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.
BENEFITS
- Competitive compensation, including meaningful equity
- (U.S. only) 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.
Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you.
At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.
We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).