ML Engineer II, Navigation

What we’re doing isn’t easy, but nothing worth doing ever is.

We envision a future powered by robots that work seamlessly with human teams. We build artificial intelligence that enables service robots to collaborate with people and adapt to dynamic human environments. Join our mission-driven team as we build out current and future generations of robots.

As an ML Engineer II (Navigation), you will develop learning-based navigation models that enable Moxi to move naturally and safely around people, beds, wheelchairs, and equipment. You’ll train policies using fleet data (imitation learning) and refine behavior with simulation and RL. Your work will directly impact delivery speed, reduced hesitation/deadlocks, and fewer interventions in real hospital deployments.

Responsibilities

• Develop learning-based navigation models that predict safe, smooth trajectories from sensor inputs and/or perception representations.
• Build imitation learning pipelines from fleet logs (trajectory extraction, filtering, scenario balancing, evaluation).
• Implement simulation-based refinement (RL, reward shaping, domain randomization) to improve robustness.
• Define navigation success metrics aligned to product outcomes.
• Collaborate with the AI Platform team to integrate learned policies behavior/safety systems and validate on-robot.
• Build regression tests and scenario replay suites for challenging scenarios.
• Analyze field behavior, identify failure modes, and close the loop through data curation and retraining.

Basic Qualifications

• Bachelor’s or Master’s degree in Robotics, Computer Science, Electrical Engineering, or related field (PhD a plus).
• 3+ years of experience in ML for robotics, autonomy, or sequential decision-making.
• Strong proficiency in PyTorch and experience with sequence models / policy learning.
• Experience with imitation learning and/or reinforcement learning in robotics or autonomy contexts.

Preferred Qualifications

• Experience with socially-aware navigation, dynamic obstacle avoidance.
• Experience with RL at scale (simulation rollouts, distributed training, stability/debugging).
• Familiarity with ROS navigation stacks and safety constraints for mobile robots.
• Experience building eval harnesses (offline replay, scenario libraries).
• Experience with Vision-Language-Action (VLA) models, behavior cloning, and/or transformer/diffusion policies for robotic control.