Job Description
Our AI Research team is building end-to-end robot policies that enable dexterous manipulation in real-world 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 large-scale initiatives that deliver end-to-end manipulation policies. You will architect new model families, lead cross-functional research efforts, and guide teams in transitioning scientific advances into reliable robotic capabilities deployed in real systems.
What You’ll 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., diffusion-based policies, ACT-style agents, multimodal embodied transformers) to enable robust dexterous manipulation.
Architect end-to-end policy systems from multimodal sensing to hardware control, across simulation and real environments.
Establish standards for data generation, demonstrations, simulation fidelity, evaluation, and sim-to-real adaptation for manipulation tasks.
Guide cross-functional 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 real-time 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.
Hands-on experience with ROS/ROS2, motion planning stacks, and integration of ML components into robotic platforms.
Evidence of high-impact contributions: top-tier publications, influential open-source efforts, field deployments, or foundational algorithms.
Proven ability to lead ambiguous multi-disciplinary 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 multi-robot deployment.
Deep expertise in at least one advanced area:
Dexterous manipulation / affordance-driven action
Multi-step task sequencing / long horizon reasoning
Offline RL or imitation learning at scale
Physics-informed or tactile-informed policies
Foundation models for embodied agents
Strong industry or academic presence: invited talks, program committees, major open-source contributions, or collaborations with robotics research institutions.
Why Join Us
You will shape how robots behave in the physical world—designing the foundational learning systems that enable dexterous, autonomous manipulation across GM’s future robotics platforms.
Location: This role is categorized as hybrid. This means the successful candidate is expected to report to the MTV office three times per week or any other frequency dictated by the business.
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 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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