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

Job 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 AIintegrating 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 controlworking closely with robotics engineers AI infrastructure teams and production partners to bring research into deployment.

What Youll Do

• 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 architecturesLLMs VLMs VLAs diffusion modelsto 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.

Required Qualifications

• Pursuing a PhD in a relevant STEM field (e.g. Computer Science Electrical/Mechanical Engineering Robotics or related discipline) or pursuing a Masters 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 systemsincluding 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.

Preferred Qualifications

• 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.

Why Join Us

Youll be part of a mission-driven team transforming how AI interacts with the physical world. This role offers the opportunity to design foundational robotic learning models collaborate with world-class experts and see your innovations deployed on real robotic systems across GMs global ecosystem.

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.

About GM

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Benefits Overview

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