Diligent Robotics - ML Engineer II, Navigation
Requirements
• 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. • 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.
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.
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