Diligent Robotics
ML Engineer II, Navigation
Remote Machine Learning Engineer role with clear candidate location fit.
PostedJul 5, 2026
Eligible countries1 accepted country
Seniority signalSenior
Work settingRemote
Accepted candidate locations
USA
Role overview
ML Engineer II, Navigation
Requirements and responsibilities
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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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