Define and pursue a research agenda spanning both pure 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, 1T+ parameter models)
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 production systems
Mentor junior researchers and shape the technical direction of the research organization as it grows

Requirements

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
Experience with production ML systems and an understanding of the constraints that cause 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

Benefits

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningexperimental designmulti-node systemsparameter modelsproduction ML systemsinterpretabilityreinforcement learningtraining methodologyempirical resultsopen-source contributions

Soft Skills

judgment about problem selectionmentoringcollaborationtechnical directionadaptability in startup environmentscommunity building

Certifications

PhD in machine learning