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Robotics ML Engineer (Simulation and Robot Learning)

Noble Machines· Software Systems
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Sunnyvale, CA$170k - $400kFull-timePosted 2mo ago

About the role

Simulation and Robot Learning role focused on solving the sim-to-real challenge by improving simulation fidelity, data generation, and training conditions to transfer policies reliably to hardware. End-to-end learning pipeline ownership from simulation to deployment on physical robots.

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.

Qualifications

  • Demonstrated ability to improve sim-to-real transfer by changing the simulation, data-generation process, or training distribution.
  • Hands-on experience training or adapting VLA policies, WAMs, language-conditioned manipulation policies, or other visuomotor policies, with strong practical understanding of imitation learning and reinforcement learning.
  • Experience building simulation environments or demonstration-generation pipelines for robot learning using tools such as Isaac Sim / Isaac Lab, MuJoCo, robosuite, or comparable systems.
  • Substantial ownership of a robot-learning pipeline across data, training, evaluation, and hardware deployment (bonus).
  • Strong Python and PyTorch skills, with the discipline to build reproducible training, dataset, and evaluation workflows. Ability to read/modify C++ for robotics software.
  • Practical understanding of robot kinematics, coordinate frames, camera calibration, control interfaces, and how embodiment affects learning and transfer.
  • A PhD, MS, or equivalent hands-on experience in robotics, machine learning, computer science, or related field.

Skills

Required:PythonPyTorchImitation LearningReinforcement LearningSimulation ToolsVision-Language-Action (VLA) Policies
Nice to have:C++

Benefits

  • Bonus, equity, and benefits as part of total rewards package.
  • Comprehensive healthcare benefits.
  • Retirement plan contributions.
  • Performance-based incentives.
Last updated Sep 27, 2026. Interested? Apply on the company site.
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Designed for people. Engineered for industry. Powered by purpose.

Noble Machines

General-purpose robotsAutomationAI-driven whole body controlRoboticsIndustrial autonomy
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