Applied Researcher - Deployment Intelligence & Continuous Learning
On-siteRedwood City, California, United States
Job Summary
Design and ship pipelines that turn real deployment data into targeted fine-tuning and online policy improvement, closing the loop from field to model. Mine high-frequency multimodal sensor and video data across tens of thousands of fleet episodes to catch failure modes, drift, and regressions before they become customer-visible. Apply reinforcement learning to improve policies directly from real-world deployment data and build automated monitoring that flags anomalies and decides what needs human versus system self-correction. Characterize generalization gaps as robots move to new sites and partner with Research, Data, and Deployment teams to turn findings into shipped improvements. This hands-on, ship-it role focuses on landing real improvements on the fleet rather than producing research for its own sake.
Required Qualifications
- Bachelor's, Master's, or PhD in CS, Robotics, Statistics, or a related field, or equivalent practical experience
- Hands-on experience in at least two of: reinforcement learning, sensor-data modeling/anomaly detection, vision-language models, or continual/online learning
- Experience building monitoring, evaluation, or data pipelines for a live ML system
- Comfortable designing and reading production experiments (A/B tests, canary rollouts, staged fleet deployments)
- Strong Python and PyTorch (or JAX)
- Comfortable with large multimodal datasets and distributed compute (Slurm/GPU clusters)
- Able to turn a fleet-scale data investigation into a clear recommendation that researchers and operators can act on
Desired Qualifications
- Experience with robot fleets or other physically-deployed autonomous systems in the field, not just simulation
- Experience building or fine-tuning perception or foundation models for automated monitoring, captioning, or anomaly detection
- Background in statistical methods for detecting anomalies and drift (change-point detection, forecasting) applied to sensor or telemetry data
- Experience with human-in-the-loop learning: reward modeling from operator corrections, active learning, or data curation from failure cases
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