Sr. ML Engineer, Autonomous Navigation

Diligent Robotics • The Village • Remote (Work from Home)

💼 Full-time🗓 2026-06-25

Core

Develop learning-based navigation models enabling service robots to move naturally and safely around people and equipment in dynamic hospital environments.

Role type

Senior IC machine-learning engineer (autonomous navigation)

Builds

Learning-based navigation policies for service robots (Moxi)

Domain

Robotics / Healthcare

Deliverable

production ML models

Required skills

imitation learning, reinforcement learning, PyTorch, sequence models, policy learning, trajectory prediction, scenario balancing, reward shaping, domain randomization

Preferred skills

socially-aware navigation, dynamic obstacle avoidance, RL at scale, ROS navigation stacks, eval harnesses

Technologies

PyTorch, ROS

Responsibilities

Develop learning-based navigation models that predict safe, smooth trajectories; Build imitation learning pipelines from fleet logs; Implement simulation-based refinement (RL, reward shaping, domain randomization); Define navigation success metrics aligned to product outcomes; Collaborate with the AI Platform team to integrate learned policies and validate on-robot; Build regression tests and scenario replay suites; Analyze field behavior, identify failure modes, and close the loop through data curation and retraining.

Rewrite

## About the role 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 a Sr. ML Engineer, Autonomous 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). - 5+ years of experience in ML for robotics and/or autonomous vehicles. - Experience with Vision-Language-Action (VLA) models, behavior cloning, and/or transformer/diffusion policies for robotic control. - 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).

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