2026 Summer Intern - Robotics Engineer (Master's or PhD)

General Motors•Mountain View, CA

23h•Hybrid

About The Position

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 Robotics Engineer Intern, 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.

Requirements

Pursuing a PhD in a relevant STEM field (e.g., Computer Science, Electrical/Mechanical Engineering, Robotics, or related discipline), or pursuing a Master’s degree with equivalent industry experience in applied robotics or robot learning with a planned graduation date between December 2026 - June 2027.
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.

Nice To Haves

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.

Responsibilities

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.