Robotics ML Engineer (Simulation and Robot Learning)

Noble Machines builds multipurpose robots for difficult industrial work and is seeking a Robotics ML Engineer to advance reliable autonomy. The role owns the end-to-end robot-learning pipeline from simulation and data generation through policy training, evaluation, deployment, and iterative improvement on physical robots, with a focus on sim-to-real transfer.

Responsibilities:

• Lead simulation-side sim-to-real development. Use hardware evidence to identify consequential gaps in visual observations, physics, contact, sensing, and control execution; improve simulation fidelity, calibration, and randomization to address them
• Build and maintain simulation environments and data-generation pipelines for robot learning. Design representative tasks, variations, and evaluation conditions, and ensure generated data is suitable for training transferable policies
• Train and adapt vision-language-action (VLA) policies, world-action models (WAMs), and other visuomotor policies using simulated and real robot data. Build reproducible experiments and make informed choices about data quality, coverage, and training methods
• Close the learning loop between simulation and hardware: analyze failures, collect corrective data, retrain, and validate improvements. Apply DAgger-style data aggregation and human-in-the-loop learning where appropriate
• Use imitation learning and reinforcement learning to improve policy performance, including RL fine-tuning and learning from real-world experience where appropriate
• Design controlled experiments that isolate transfer bottlenecks and distinguish simulator limitations from data, policy, and integration issues. Prioritize simulation improvements by their measured effect on hardware
• Develop repeatable evaluation and regression tests for robustness and generalization. Establish how well simulation results predict hardware performance, and investigate discrepancies
• Deploy, profile, and debug learned policies on robot compute. Improve inference efficiency and reliability, and work with controls and systems teammates to integrate policies with the robot
• Own data and experiment quality across the pipeline, including curation, versioning, reproducibility, and clear reporting of results