Research and prototype advanced machine learning methods for perception, prediction, planning, or decision-making.
Design, train, evaluate, and improve models using large-scale multimodal driving and sensor data.
Build data pipelines, experimentation workflows, and evaluation tools for rapid model iteration.
Validate models through simulation, testing, performance analysis, and failure-case investigation.
Optimize models and systems for scalability, latency, reliability, and deployment constraints.
Collaborate with engineering and research partners to integrate prototypes into autonomous vehicle systems.
Document findings, present technical insights, and contribute to publications or other research outputs.
Currently enrolled full-time in a PhD program in computer science, machine learning, artificial intelligence, robotics, engineering, or a related STEM field.
Must have at least one additional quarter/semester of school remaining following the completion of the internship
Demonstrated depth of AI/ML research through publications, research projects, advanced coursework, or equivalent technical work.
Strong understanding of modern machine learning and deep learning methods.
Proficiency in Python and hands-on experience with an ML framework such as PyTorch, TensorFlow, or JAX.
Experience designing experiments, analyzing results, and applying quantitative problem-solving methods.
Strong programming, debugging, and software development fundamentals.
Strong communication skills and the ability to collaborate across research and engineering disciplines.
Availability to work full-time, 40 hours per week, during the internship period.
Candidates are not expected to meet every qualification listed below. Depending on the needs of a specific Autonomous Vehicles team, a combination of these skills and areas of knowledge may be relevant.
Conduct research in autonomous vehicles, ADAS, robotics, computer vision, or embodied AI.
Demonstrate knowledge of foundation models, transformers, generative and diffusion models, or vision-language architectures.
Apply multimodal learning techniques to camera, lidar, radar, or other sensor data.
Use self-supervised, imitation, reinforcement, or deep reinforcement learning methods.
Train models on large-scale datasets using distributed, parallel, or high-performance computing environments.
Develop ML systems, data pipelines, experimentation frameworks, or production-oriented model infrastructure.
Validate models through simulation, closed-loop environments, or real-world driving scenarios.
Apply expertise in trajectory prediction, behavior planning, motion planning, perception, mapping, or structured scene understanding.
Program in C++ or other systems programming languages.
Optimize models and systems using GPU programming, CUDA, accelerator frameworks, or performance profiling.
Apply numerical optimization, statistical estimation, probabilistic modeling, and systems-level tradeoff analysis.
Demonstrate a strong research record through first-authored publications, grants, fellowships, patents, or open-source contributions.
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 $12,300-$14,600 per month. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
GM will provide a one-time lump sum taxable stipend payment to eligible students selected for the 2027 Student Program - to help facilitate administration of relocation benefits, please apply using the permanent address you would move from.
Paid US GM Holidays
GM Family First Vehicle Discount Program
Result-based potential for growth within GM
Intern events to network with company leaders and peers
Start dates for this internship role are May 24th and June 14th of 2027.
Assignments will be based on team placement to ensure interns are located with their working team and leader.
Hybrid : This role is categorized as hybrid. This means the successful candidate is expected to report to their designated work location three times per week, at minimum.
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We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.
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