Post-training Research Scientist

Baseten · Data AI · San Francisco, CA · EPD

Research Scientist focused on post-training methodology and performant inference, with a significant portion dedicated to pure research and the remainder to applied research informing the company's platform and customer needs. The role involves designing and executing experiments, publishing at top venues, and collaborating with engineering teams to translate research into production systems.

What you'd actually do

  1. Define and pursue a research agenda spanning both pure and applied work, with the applied component connected to Baseten's platform and customer needs
  2. Design and execute rigorous experiments, frequently at meaningful scale (multi-node, 1T+ parameter models)
  3. Publish at top venues (NeurIPS, ICML, ICLR) and establish Baseten's research presence
  4. Collaborate with model performance and training infrastructure teams to bridge research findings and production systems
  5. Mentor junior researchers and shape the technical direction of the research organization as it grows

Skills

Required

  • PhD or equivalent research depth in machine learning
  • First-author publications at top venues
  • Ability to move from theory through implementation to empirical results
  • Judgment about problem selection
  • Ability to distinguish research that advances a metric from research that changes how systems are built
  • Willingness to operate in a startup environment

Nice to have

  • Experience with production ML systems
  • Understanding of constraints causing academic solutions to fail in deployment
  • 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

What the JD emphasized

  • 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, 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

Other signals

  • research agenda
  • performant inference
  • production systems
  • publish at top venues