AI Research Engineer, Model Scaling, Self-Driving

At Tesla, you will have access to unparalleled resources that set us apart from other companies in the AI industry. You will have access to the largest self-driving dataset in the world, providing a unique, and perhaps the only, environment to investigate scaling laws for sequential decision-making problems. Tesla also offers one of the highest GPU resources per engineer in the industry, giving you a significantly larger computational budget compared to typical AI research environments.

These unique resources enable you to conduct experiments and scaling analyses at a level unmatched by any other company, allowing you to grow as a researcher by tackling challenges that others simply cannot offer. By working at Tesla, you’ll have the opportunity to push the boundaries of AI and autonomous driving technology while advancing your skills in an environment that truly values innovation and cutting-edge research.

• Perform scaling law analyses on model size, data size, data mixture, training compute, and other critical parameters to optimize our AI models using the largest self-driving dataset in the world
• Develop and implement novel architectures and algorithms to effectively scale large End-to-End (E2E) self-driving models
• Create and maintain infrastructure for efficient, large-scale distributed training of E2E models, resolving compute and memory bottlenecks for training and inference
• Evaluate and enhance model performance, with a focus on increasing miles driven without human intervention
• Work closely with cross-functional teams to deploy AI models in production, ensuring they meet stringent performance and reliability standards
• Contribute to the development of tools and frameworks that improve the scalability and efficiency of model training and deployment processes

• Proven experience in scaling and optimizing large AI models, with a strong understanding of infrastructure challenges and solutions
• Proficiency in Python and a deep understanding of software engineering best practices
• In-depth knowledge of deep learning fundamentals, including optimization techniques, loss functions, and neural network architectures
• Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX
• Strong expertise in distributed computing and parallel processing techniques
• Demonstrated ability to work collaboratively in a cross-functional team environment
• Strong problem-solving skills and the ability to troubleshoot complex system-level issues