To help facilitate administration of relocation benefits if you are selected, please apply using the permanent address you would move from.
Work Arrangement:
Hybrid: This internship is categorized as hybrid. The selected intern is expected to report to the office up to three times per week or as determined by the team.
Locations:
San Francisco, California
Sunnyvale, California
Mountain View, California
About the Team:
The Scaling Foundations team is dedicated to building the ML technologies and cutting edge ML algorithms that enable us to go up the scaling curves for large driving models for building highly performant autonomous vehicles. We are a collaborative, forward-thinking group of researchers and engineers tackling some of the most complex challenges in autonomy and machine learning.
About the Role:
As an AI/ML Engineering Intern on the Model Scaling Foundations team, you’ll work on cutting-edge projects advancing vehicle autonomy, developing algorithms and models that shape the future of self-driving technology. This internship provides experience with real-world AI/ML systems, access to very large datasets of autonomous driving, collaboration with leading researchers and engineers, and mentorship from experienced AV researchers to grow your skills in the autonomous vehicle industry.
What You’ll Do:
Lead research and prototyping of advanced machine learning methods, such as foundation models, vision-language architectures, diffusion models, image/video generation, self-supervised learning, imitation learning, and reinforcement learning.
Prototype ML models that improve perception, prediction, or decision-making for autonomous driving.
Work with very large datasets containing diverse road driving conditions and driving behaviors and build our large driving models with these datasets.
Collaborate with cross-functional teams, including perception, robotics, and systems engineering.
Participate in technical discussions, share insights, and work towards publishing results.
Required Qualifications:
Currently pursuing or in the process of obtaining a Ph.D. in Machine Learning, Artificial Intelligence, Computer Science, or a related technical field.
Solid understanding of modern machine learning techniques, especially deep learning architectures (e.g., transformers, generative models, multimodal learning).
Proficiency in Python and ML frameworks such as PyTorch or TensorFlow.
Research experience in AI/ML, demonstrated through coursework, academic projects, or publications.
Experience working with large datasets, running parallel
Strong problem-solving skills and a collaborative mindset.
Strong communication and presentation skills.
Experience working and communicating cross functionally in a team environment.
Able to work fulltime, 40 hours per week
Preferred Qualifications:
Familiarity with autonomous vehicles or advanced driver assistance systems (ADAS).
Experience working with large-scale datasets and training ML models in high-performance computing environments.
Intent to return to degree program after the completion of the internship/co-op
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, CVPR, ICML, ICLR, AAAI, ECCV, RSS, ICRA, CoRL, or similar.
Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches
Experience building systems based on machine learning, reinforcement learning and/or deep learning methods
Compensation:
The monthly salary for this role is $13,100 per month
GM will provide a one-time lump sum taxable stipend payment to eligible students selected for the 2026 Student Program.
What you’ll get from us (Benefits):
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
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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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