Job Description

Our AI Research team is building end-to-end robot policies that enable dexterous manipulation in realworld environments. We are advancing embodied AI by integrating multimodal perception, robot learning architectures, and physical execution systems to solve manipulation, autonomy, and simulation challenges at an industrial scale.

As a Principal Applied Scientist, you will define technical direction, create breakthrough robot learning methodologies, and drive the execution of largescale initiatives that deliver endtoend manipulation policies. You will architect new model families, lead crossfunctional research efforts, and guide teams in transitioning scientific advances into reliable robotic capabilities deployed in real systems.

What Youll Do

• Define the research strategy and technical roadmap for embodied manipulation policies that span perception, training, control, and deployment.
• Invent or advance robot learning architectures (e.g., diffusionbased policies, ACTstyle agents, multimodal embodied transformers) to enable robust dexterous manipulation.
• Architect endtoend policy systems from multimodal sensing to hardware control, across simulation and real environments.
• Establish standards for data generation, demonstrations, simulation fidelity, evaluation, and simtoreal adaptation for manipulation tasks.
• Guide crossfunctional efforts to scale robot learning from prototypes to production, including reliability constraints, observability, and performance guarantees.
• Mentor scientists and engineers, influence hiring, and shape research priorities within the organization.
• Represent GM in external communities (e.g., CoRL, RSS, ICRA, NeurIPS), pursue collaborations, and drive thought leadership in embodied AI.

Required Qualifications

• PhD in a relevant STEM field, with a recognized track record in robotics, robot learning, or embodied AI.
• Significant experience designing and deploying robot learning systems on physical hardware, including manipulation tasks and realtime policy execution.
• Expertise in modern AI architectures (Transformers, diffusion models, VLM/VLA agents, imitation learning, offline RL) and ability to extend them to new embodied scenarios.
• Mastery of PyTorch, including model internals, performance optimization, distributed training, and debugging complex failures.
• Handson experience with ROS/ROS2, motion planning stacks, and integration of ML components into robotic platforms.
• Evidence of highimpact contributions: toptier publications, influential opensource efforts, field deployments, or foundational algorithms.
• Proven ability to lead ambiguous multidisciplinary research programs and deliver tangible embodied AI outcomes.

Preferred Qualifications

• Experience establishing research agendas in robot learning or dexterous manipulation that resulted in widely adopted systems or protocols.
• Leadership of projects involving perception policy control integration, staged training, or multirobot deployment.
• Deep expertise in at least one advanced area:
  • Dexterous manipulation / affordancedriven action
  • Multistep task sequencing / longhorizon reasoning
  • Offline RL or imitation learning at scale
  • Physicsinformed or tactileinformed policies
  • Foundation models for embodied agents
• Strong industry or academic presence: invited talks, program committees, major opensource contributions, or collaborations with robotics research institutions.

Why Join Us

You will shape how robots behave in the physical worlddesigning the foundational learning systems that enable dexterous, autonomous manipulation across GMs future robotics platforms.

Location

This role is categorized as hybrid. The successful candidate is expected to report to the MTV office three times per week or any other frequency dictated by the business.

Compensation

Compensation information is a goodfaith 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 New York, Colorado, California, or Washington.

• The salary range for this role is 259,000 to 320,000. 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.

Company Vehicle

Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, through which you will be assigned a General Motors vehicle to drive and evaluate.

Note: program participants are required to purchase/lease a qualifying GM vehicle every four years unless one of a limited number of exceptions applies.

Relocation

This job may be eligible for relocation benefits.

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