Robotics ML Engineer (Simulation and Robot Learning)

Jobverse.io; Sunnyvale (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

Noble Machines 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.

You will lead simulation-side development, build data-generation pipelines, train visuomotor policies, and integrate learned policies with robot hardware while improving inference efficiency and

Qualifications

• Experience with robotics simulation and data-generation pipelines.
• Familiarity with sim-to-real transfer challenges.
• Proficiency in designing visuomotor policies.
• Ability to perform controlled experiments and evaluate outcomes.

Responsibilities

• Lead simulation-side sim-to-real development. Use hardware evidence to identify gaps in visual observations, physics, contact, sensing, and control execution; improve simulation fidelity, calibration, and randomization.
• 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

Skills

Simulation development

Sim-to-real transfer

Robot learning

Policy training

Imitation learning

Reinforcement learning

Experiment design

Data generation

Data curation

Deployment on hardware

Job description

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