Applied Researcher - Deployment Intelligence

Job description

Job responsibilities

• Continuous Learning Loops: Design and ship pipelines that turn real deployment data (successes, failures, teleop corrections) into targeted fine-tuning and online policy improvement, closing the loop from field to…
• Fleet Data Analytics: 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.
• RL for Deployment: Apply reinforcement learning (offline RL, RL fine-tuning, reward modeling from human and teleop feedback) to improve policies directly from real-world deployment data, not just simulation.
• Automated Fleet Monitoring: Build automated monitoring that flags anomalies, near-failures, and out-of-distribution scenes across the fleet in real time, and that decides what needs a human versus what the system can…
• Cross-Scene Generalization: Characterize and close generalization gaps as robots move to new sites, lighting, layouts, and objects;
• End-to-End Ownership: Partner with Research, Data, and Deployment teams to turn a finding into a shipped improvement, from a data-analysis notebook to a production monitoring dashboard to a deployed model update.