AI Researcher (AV) - Summer Internship

GM Israel (Herzliya) takes a significant part in introducing sophisticated software features into car in many domains like connected cars, advanced camera technologies and research. We shape the face of the future vehicles in diverse fields by developing cutting edge technologies that define how drivers experience safety, awareness, and confidence on the road.

GM is committed to Zero Emission, Zero Crashes and Zero Congestion vision. 

About the Group:

At the autonomous vehicles  Foundational AI Research  team in Israel, which is part of the GM AV  AI Research organization , we are shaping  next-generation  autonomous driving, L3 and  beyond . 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.  

The mission of the  AI Research group  based in the US,  is to create groundbreaking technologies  for next-generation  autonomous vehicles  by combining cutting-edge research with real-world innovation and technology incubation. Among several  initiatives , we are building  multimodal foundation models (vision–language–action)  over  fleet- and internet-scale  data, and we are building  controllable world models  that generate realistic, multi-sensor driving data (camera, radar, LiDAR) to  strengthen coverage and accelerate long-tail reduction  for our L3 autonomy product.

About the Role  

This position is limited to the three-month academic summer break. Students are expected to work full-time in a hybrid setup, including at least three onsite days per week. 

As an AI/ML Research Intern on the AI Research 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.  

What will YOU 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.  

• Collaborate with cross-functional teams, including perception, robotics, and systems engineering.  

• Participate in technical discussions, share insights, and work towards publishing results.  

Your Skills & Abilities (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

• Able to work fulltime during the academic summer break 

• 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

What Can Give You a Competitive Edge (Preferred Qualifications)

• Intent to return to degree program after the completion of the internship 

• 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.  

• 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