Robotics ML Engineer - Sim-to-Real Learning & Autonomy

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 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