2027 Summer Intern – AI/ML Engineer, Autonomous Vehicles: Simulation (PhD)

General Motors•Sunnyvale, CA

3d•$12,300 - $14,600•Hybrid

About The Position

As a PhD AI/ML Engineering Intern within General Motors’ Autonomous Vehicles organization, you will advance reliable AI/ML capabilities by translating deep research into validated, production-oriented systems. Your work may support perception, prediction, planning, decision-making, embodied AI, simulation, sensor understanding, mapping, or large-scale model development. You will work with real-world driving and sensor data, modern machine learning methods, and autonomous vehicle evaluation environments. You will collaborate with researchers and engineers to design experiments, build and assess models, investigate failure cases, and integrate promising approaches into broader AV systems.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.