2026 Summer Intern – AI/ML Intern – Model Scaling Foundations (PhD)

Job Description

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.