Sr. Research Scientist - Agent Flywheel
Databricks · New York City, New York
About this role
**Sr. Research Scientist — Agent Flywheel** **Company Description** At Databricks, we enable data teams to solve the world’s toughest problems—from security threat detection to cancer drug development—by building and running the world’s best data and AI platform. The Databricks AI Research organization is pushing the frontier of next-generation enterprise AI. We believe a company’s data is its greatest competitive advantage, and we’re building the models and agents that unlock it, spanning the full stack from model training to advanced multi-agent systems. **Job Description** As a Research Scientist on the Agent Flywheel team, you will build the context layer for recursive self-improvement and autofix loops for agents. You’ll turn 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—then close the loop so those signals drive training and agent improvement. **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 beyond the state of the art. **What we look for** - Expertise in debugging and diagnosing failures in complex, stochastic, multi-component systems. - Strong instinct for designing metrics and evals that track what actually matters; ability to question noisy, sparse, or biased signals. - Experimental rigor and causal thinking (offline ablations to online experiments). - 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. - Drive to get your work into the hands of real users. **Nice to have** - Experience with agent systems (coding agents, long-horizon tool-using agents, or building evaluation environments). - A track record of novel research on learning from feedback (post-training, RLHF, reward modeling).
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