Senior Applied Scientist - AI & Robotics

Description

The AI Research team is advancing how intelligent robotic systems perceive, act, and adapt in the physical world. We are pioneering the next generation of embodied AI-integrating multimodal foundation models, robot learning architectures, and real-world deployment to solve manipulation, planning, and simulation challenges at an industrial scale.

As a Senior Applied Scientist, you will lead the development of end-to-end AI systems that enable dexterous manipulation, autonomous behaviors, and multimodal understanding on physical robotic platforms. You will design, prototype, and implement cutting-edge models spanning perception, policy learning, 3D reasoning, and control-working closely with robotics engineers, AI infrastructure teams, and production partners to bring research into deployment.

• Design and implement advanced robot learning architectures (e.g., diffusion policies, ACT, VLM/VLA agents, imitation learning) to support manipulation, path planning, and autonomous execution.

• Build end-to-end model training pipelines for robotics applications, integrating multi-modal sensor data such as RGB, depth, force/torque, LiDAR, and proprioceptive signals.

• Develop scalable policy inference and control loops, pairing high-level perception with motion planning and on-robot execution.

• Apply or extend large-scale architectures-LLMs, VLMs, VLAs, diffusion models-to embodied tasks, sim-to-real adaptation, and grounding.

• Collaborate with cross-functional teams to translate research prototypes into deployable robotics software, ensuring robustness, efficiency, and safety.

• Design data collection, demonstration strategies, and simulation frameworks to support offline training, imitation learning, and hardware validation.

• Stay current with state-of-the-art advancements in embodied AI, robot learning, and manipulation, and share findings through internal research discussions and presentations.

• PhD in a relevant STEM field (e.g., Computer Science, Electrical/Mechanical Engineering, Robotics, or related discipline), or a Master's degree with equivalent industry experience in applied robotics or robot learning.

• Proven experience in building and deploying ML models on robotic systems-including training, evaluation, and integration with real or simulated platforms.

• Deep understanding of modern AI architectures (e.g., Transformers, VLMs/VLAs, diffusion models, CNNs) and hands-on experience training models at scale.

• Strong implementation ability in PyTorch, including writing custom modules, batching, debugging, and performance/efficiency considerations.

• Practical experience with ROS/ROS2 or robotics middleware and integrating learning components into manipulation or motion-control workflows.

• Demonstrated research impacts through robotics/ML publications or contributions to production-grade robotics systems.

• Ability to translate ambiguous embodied AI problems into well-scoped experiments, and maintain rigorous evaluation, ablation, and statistical validation practices.

• Experience developing robot learning systems for manipulation, motion planning, or autonomous behaviors (e.g., diffusion policies, ACT, behavioral cloning, offline RL).

• Hands-on expertise with robotics perception, including 3D understanding, depth/RGB fusion, multimodal grounding, or force/torque sensing.

• Familiarity with simulation environments such as Isaac Sim, Mujoco, Gazebo, or PyBullet, and demonstrated experience with sim-to-real transfer strategies.

• Working knowledge of robotics middleware (ROS/ROS2) and integration of ML components into real-time robotic stacks.

• Experience building or adapting foundation models for embodied tasks (VLMs/VLAs, multimodal diffusion, instruction-following agents).

• Track record of production-ready robotics systems, open-source contributions, or publications in top-tier robotics/AI venues.

• The salary range for this role is $150,000 - $195,000. 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.

• 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.

• This job may be eligible for relocation benefits.