Base Labs Research Scientist

About Baseten

Baseten powers mission-critical inference for 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.

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.

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.