2026 Summer Intern – AI/ML Intern – Sensor Encoder (PhD)

GM•Sunnyvale, CA

3d•Hybrid

About The Position

About the Team: The Sensor Encoder team is dedicated to advancing the state-of-the-art in AI for autonomous vehicles, with a specific focus on encoders for camera , lidar and radar . We are a collaborative, forward-thinking group of researchers and engineers tackling some of the most complex challenges in autonomy and machine learning related to sensor data processing. About the Role: As an intern on the sensor encoder 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, collaboration with leading researchers and engineers, and mentorship from experienced AV researchers to grow your skills in the autonomous vehicle industry.

Requirements

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.
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

Nice To Haves

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
Graduating between December 2026 and June 2027.

Responsibilities

Lead research and prototyping of advanced machine learning methods, such as foundation models, vision-language architectures , self -supervised learning.
Prototype ML models that improve perception , prediction, or decision-making for autonomous driving.
Collaborate with cross-functional teams, including research, robotics, and systems engineering.
Participate in technical discussions, share insights, and work towards publishing results.