Internship, AI Engineer, Tesla AI (Fall 2026)

Consider before submitting an application:

This position is expected to start August or September 2026 and continue throughfall term (endingapproximately December 2026) or continuing into Winter/Spring 2027 if available and there is an opportunity to do so. We ask for a minimum of12 weeks, full-time (40 hours/week) and on-site, for most internships.Our internship program is for students who are actively enrolled in an academic program.Recent graduates seeking employment after graduation and not returning to school should apply for full-time positions, not internships.

International Students: If your work authorization is through CPT, please consult your school on your ability to work 40 hours per week before applying. You must be able to work 40 hours per week on-site. Many students will be limited to part-time during the academic year.

The Internship Recruiting Team is driven by the passion to recognize and develop emerging talent. Our year-round program places the best students in positions where they will grow technically, professionally, and personally through their experience working closely with their Manager, Mentor, and team. We are dedicated to providing an experience that allows the intern to experience life at Tesla by including them in projects that are critical to their team's success.

About the Team

Tesla is seeking exceptional Machine Learning Interns to help build large scale models to drive the future of autonomy across all current and future generations of Tesla AI products. You will work on a lean team without boundaries and have access to one of the world's largest training clusters with a data engine that constantly generates new information for improving our models. Most importantly, you will see your work repeatedly shipped to and utilized by millions of Tesla's customers.

We are seeking Interns in the following AI disciplines:

• Train large-scale foundation and generative models that are optimized for performance and latency
• Improve data engine for large scale and high-quality dataset curation
• Reinforcement Learning for instilling objectives and improving overall robustness, including RL for training
• Design compound AI systems for better planning and reasoning, with emphasis on world modeling and generative videos

• Applied research in the areas of Foundation Models, including but not limited to computer vision, large language models and generative modeling
• Work on cutting-edge techniques in AI - multi-task learning, video networks, multi-modal generative models, imitation learning, reinforcement learning, semi-supervised learning, self-supervised learning
• Explore and implement novel AI tooling and techniques for efficient training and fine-tuning of large-scale models, incorporating RL for training
• Leverage millions of miles of driving data and interventions to build a robust and scalable end-to-end learning based self-driving system
• Collaborate with a team to apply research findings to real-world challenges, ensuring high-quality system integration within existing platforms
• Experiment with data generation and network driven data collection approaches to enhance the diversity and quality of training data, including generative videos
• Ship production quality, safety-critical software to the entirety of Tesla's vehicle fleet, with applications in world modeling

• Demonstrated experience in machine learning frameworks and models such as PyTorch, TensorFlow, GPT, CNNs, and generative models
• Strong experience with Python and software engineering best practices
• Experience with one or more of imitation Learning, reinforcement learning (offline/off-policy), modern neural network architectures (e.g., GPT, diffusion, generative models), or related techniques, including RL for training
• An "under the hood" knowledge of deep learning: layer details, loss functions, optimization, etc.
• Prior experience with sparse training techniques, neural network pruning, and generative modeling, with exposure to generative videos
• Experience with training large models on distributed computing
• Ability to work on complex problems and produce significant research and/or experience deploying production ML models at scale, particularly in world modeling
• Proven track record of innovations and executions in deep learning, demonstrated with shipping products or first-author publications at leading AI conferences