Action (Masters)

General Motors•Sunnyvale, CA

1d•Hybrid

About The Position

About the Team: The AI Research organization is dedicated to advancing the state-of-the-art in AI for 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 a VLM/VLA Research Intern on the AI Research team, you will operate at the frontier of Embodied AI, developing foundational models that bridge the gap between high-level reasoning and physical execution. Your work will focus on advancing vision-language-action architectures to solve critical challenges in data mining and end-to-end autonomous driving. This role offers a unique opportunity to work on real-world AI/ML systems at scale, collaborating with and receiving mentorship from world-class researchers to shape the future of grounded foundation models in the autonomous vehicle industry.

Requirements

Currently pursuing or in the process of obtaining a Masters 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, LLMs, VLMs, 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.
Experience adapting foundation models (VLM/VLAs, diffusion, instruction-following agents) for embodied control tasks.
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

Responsibilities

Drive the development of embodied foundation models and vision-language-action architectures that unify multimodal perception with robotic control.
Prototype and refine ML models that leverage VLA architectures to improve decision-making and reasoning for autonomous vehicles through imitation and reinforcement learning.
Utilize vision-language models and generative techniques (such as world models) to improve the model's understanding of complex driving scenarios.
Partner with perception, robotics, and systems engineering teams to integrate VLA research into the broader autonomous stack and validate models in closed-loop environments.
Engage in high-level technical brainstorming, share insights across the AI Research org, and contribute to the academic community through top-tier conference publications.