Sr. Research Scientist - Agent Flywheel

About the role

P-1213

About the role

As a Research Scientist on the Agent Flywheel team, you will build the context layer for recursive self-improvement and autofix loops for agents: turning raw production session traces from Databricks' data agents into online signals agents can optimize against. You will research how to convert messy, real customer interactions into deterministic metrics, calibrated LLM judges, and failure clusters, and close the loop so those signals drive training and agent improvement.

You will join a collaborative team of researchers with diverse backgrounds and encode your findings into products our customers rely on.

The impact you will have

• Turn raw production session traces from Genie Code and Genie One into online signals agents can optimize against.
• Train and calibrate LLM judges so model feedback becomes reliable, repeatable signal, including calibration against expert human ratings.
• Convert multi-turn agent trajectories into datasets and reward signals for post-training, closing the loop between production signals, evaluation, and agent improvement.
• Design benchmarks and RL environments from real customer workloads that probe long-horizon, tool-using agent behavior.
• Advance evaluation research: online evals on live traffic, automated raters, verifiable rewards, and measurement that keeps pace with agents that act.
• Keep up with the latest developments in deep learning and create new techniques that go beyond the state of the art.

What we look for

• Expertise in debugging and diagnosing failures in complex, stochastic, multi-component systems
• Deep instinct for designing metrics and evals that track what actually matters, and questioning noisy, sparse, or biased signals
• Experimental rigor and causal thinking, from offline ablations to online experiments
• A drive to turn measurement into improvement: closing the loop from signals to changes that move the numbers
• Solid AI research and software engineering skills
• Strong communication skills and excitement to work on a small, fast-paced team
• A drive to get your work into the hands of real users

Nice to have

• Experience with agent systems, such as coding agents, long-horizon tool-using agents, or building evaluation environments
• A track record of novel research on learning from feedback, such as post-training, RLHF, or reward modeling

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.

Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range.

The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.

Local Pay Range $164,000 - $235,000 USD

Benefits

At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.