<p>Scaling transformers, as well as more recent advances in Reinforcement Learning with Verifiable Rewards (RLVR), has created models with Ph-D level intelligence in a wide variety of subject areas from Math to Social Sciences. Yet these models continue to struggle in real-world physical reasoning, often struggling to tell left from right.</p> <p>At Tesla AI, we want to develop Olympiad-level physical intelligence that will enable highly capable robots, both wheeled and legged. These models should be able to anticipate and reason about future movements of any object or scene at the level of a race car driver or professional athlete. To accomplish this, you will have access to petabytes of multimodal (video, audio, action etc.) real-world data from our global fleet of cars and robots, as well as Teslas state-of-the-art compute resources.</p> <p>An added level of complexity with robotics is the requirement to run at real-time on local compute. This requirement acts as a forcing function for Tesla AI to develop models that optimize for the highest intelligence per byte of parameters. In this role, you will have the opportunity to work on post-training and reinforcement learning of large multimodal models with an emphasis on real-world physical intelligence. You will also get the chance to distill these large models to smaller models that will run on our state-of-the-art inference hardware.</p> <ul> <li>Create multimodal post-training and RLVR datasets that utilize Teslas fleet of cars and robots </li><li>Develop training infra necessary to run reinforcement learning on large multimodal models </li><li>Develop downstream evaluations that can guide the tuning of these large models </li><li>Distill large models into smaller models that can run in real-time on the local compute of our cars and robots </li><li>Proven experience in scaling and optimizing large AI models, with a strong understanding of infrastructure challenges and solutions, especially in the domain of reinforcement learning </li><li>Proficiency in Python and a deep understanding of software engineering best practices </li><li>In-depth knowledge of deep learning fundamentals, including distillation and reinforcement learning </li><li>Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX </li><li>Strong expertise in distributed computing and parallel processing techniques </li><li>Demonstrated ability to work collaboratively in a cross-functional team environment </li><li>Strong problem-solving skills and the ability to troubleshoot complex system-level issues </li></ul>