Applied Robot Learning Engineer / RL Specialist

General Motors LLC; Mountain View (CA); On-site; USD 153,000 - 234,000; Full time

Benefits offered by this job

Health insurance

GM vehicle discounts

Tuition assistance

Employee assistance program

Paid vacation & holidays

Job summary

General Motors Company seeks an Applied Robot Learning Engineer/RL Specialist to advance reinforcement learning for manufacturing robotics in Mountain View, CA. The role emphasizes deploying learning-based policies on physical robots, addressing sim-to-real challenges, and improving manipulation and execution through robust policies.

You will work hands-on with hardware, design reward models, and optimize policies like PPO/SAC, while collaborating with simulation and engineering teams.

Qualifications

• Ph.D. in Computer Science, Robotics or related field or Master’s with 3+ years industry experience.
• Strong applied RL for robotics, including policy optimization, reward design, training and evaluation.
• Experience deploying learned policies on physical robots; not just simulation.
• Experience with sim-to-real transfer and diagnosing discrepancies.
• Hands-on with Git, Python, PyTorch, ROS/ROS 2; familiarity with Jetson and MLOps tooling.

Responsibilities

• Develop, fine-tune, and evaluate VLA models and learning-based policies on physical robots.
• Improve manipulation success, execution efficiency, robustness, and recovery from failures.
• Design reward functions and detectors, including learned or VLM-based rewards.
• Set up safe real-robot RL with constraints, resets, and supervised exploration.
• Diagnose sim-to-real gaps and apply domain randomization and real-world fine-tuning.

Skills

Reinforcement learning for robotics

Python

PyTorch

ROS/ROS 2

NVIDIA Jetson

Experimentation

Policy optimization

Hardware debugging

Education

PhD in Computer Science/Robotics or related

Master's + 3+ years industry

Tools

Git

ROS/ROS 2

Foxglove

Edge compute (NVIDIA Jetson)

Job description

Applied Robot Learning Engineer/RL Specialist – Reinforcement Learning & Policy Optimization

We are looking for an experienced applied robotics/robot learning engineer with strong reinforcement learning expertise and hands-on experience deploying learning-based policies for complex manufacturing tasks. The role primarily focuses on improving the performance of VLA/world models, addressing sim-to-real challenges, and developing robust and scalable policies for manipulation, failure recovery, and optimal execution. This is a hands-on role requiring real-world deployment experience, comfort working directly with robotic hardware, and the flexibility to support broader engineering and experimental tasks as project priorities evolve. The ideal candidate combines strong RL fundamentals with practical robotics engineering experience and some exposure to bimanual manipulation. Approximately 80% of the role will focus on applied engineering, policy implementation, robot experiments, evaluation, and deployment, with 20% dedicated to research and technical exploration. The emphasis is on translating RL methods into measurable improvements on physical robots rather than pursuing research in isolation.

Key Responsibilities

• Develop, fine-tune, and evaluate VLA models and learning-based policies on physical robotic systems.
• Apply reinforcement learning to improve manipulation success rates, execution efficiency, robustness, and recovery from failures.
• Implement and evaluate appropriate approaches such as PPO, SAC, residual RL, policy adaptation, or learned recovery policies.
• Design reward functions and success detectors, including learned or VLM-based reward models, for real-world manipulation tasks.
• Apply sample-efficient methods for real-world training, such as RL from demonstrations, offline-to-online RL, and human-in-the-loop corrections.
• Set up safe real-robot RL, including action limits, workspace constraints, automated resets, and supervised exploration.
• Diagnose sim-to-real gaps and improve real-world performance using techniques such as domain randomization, system identification, and real-world fine-tuning.
• Apply learned policies to manipulation tasks, including tasks involving bimanual setups and coordinated robot execution.
• Design evaluations, analyze failure modes, and measure improvements in success rate, cycle time, generalization, and repeatability.
• Work directly with robotic hardware to run experiments, debug policies, and validate improvements.
• Supervise data collection, identify data-quality issues, and coordinate corrections to demonstrations or datasets.
• Collaborate with the simulation team and contribute to benchmarking, model evaluation, and other hands-on engineering tasks as needed.

Required Qualifications

• Ph.D. in Computer Science, Robotics or a related discipline or Master's with 3 or more years of industry experience.
• Strong applied expertise in reinforcement learning for robotics, including policy optimization, reward design, training, and evaluation.
• Experience deploying or validating learned policies on physical robots; simulation-only experience is insufficient.
• Experience with sim-to-real transfer and diagnosing discrepancies between simulated and real-world execution.
• Experience with Git, Python, PyTorch, and ROS/ROS 2, combined with hands-on familiarity with edge compute platforms (NVIDIA Jetson) and robotics MLOps ecosystems (experiment tracking, dataset versioning, and visualization tools like Foxglove).
• Experience with sensor calibration (eye-in-hand/eye-to-hand camera calibration), force-torque sensors, or teleoperation data collection setups.
• Understanding of imitation learning, robot kinematics, control interfaces, and manipulation workflows.
• Comfort working directly with robots and taking ownership of experiments, model training, evaluations, data quality, debugging, and deployment.
• Adaptability and willingness to contribute beyond core model development when required to meet project goals.

Preferred Qualifications

• Experience combining RL with pretrained VLA policies or imitation learning.
• Experience with residual RL, online policy adaptation, failure recovery, or contact-rich manipulation.
• Experience with VLA models such as Pi0/Pi0.5, OpenVLA, Groot, or comparable architectures.
• Experience with industrial or collaborative robots and real-world manufacturing applications is a plus. Hands-on experience with Fanuc/UR robots is a plus.

Collaboration & Working Style

• Ownership and Accountability: Take ownership of assigned tasks from problem identification through implementation, testing, and deployment. Proactively identify issues, follow through action items, and communicate blockers early.
• Teamwork: Work effectively with cross-functional teams. Share knowledge, provide constructive feedback, and contribute toward shared project goals.
• Passion for Experimentation: Enjoy testing ideas rapidly, working directly with robots, debugging failures, and turning promising approaches into working solutions. Be willing to iterate repeatedly and learn from unsuccessful experiments.
• Adaptability: Be willing to take on responsibilities beyond core model development, including evaluation, data quality checks, debugging, benchmarking, and experimental setup, based on project priorities.

On-site 5x days per week, Mountain View, CA

Compensation

The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of the California Bay Area. The salary range for this role is $153,200 to $234,100. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.

Bonus Potential

An incentive pay program offers payouts based on company performance, job level, and individual performance.

Benefits

GM offers a variety of health and wellbeing benefit programs.

• Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

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