<p>Consider before submitting an application:</p> <p>This position is expected to start around January 2026 and continue through the Winter/Spring term (approximately April 2026) or into Summer 2026 if available and there is an opportunity to do so. We ask for a minimum of 12 weeks, full-time 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.</p> <p>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. to experience life at Tesla by giving them ownership over projects that are critical to their teams success.</p> <p>In this role, you will be responsible for the internal working of the AI inference stack and compiler running neural networks in millions of Tesla vehicles and Optimus. You will collaborate closely with the AI Engineers and Hardware Engineers to understand the full inference stack and design the compiler to extract the maximum performance out of our hardware.</p> <p>The inference stack development is purpose-driven: deployment and analysis of production models inform the team's direction, and the team's work immediately impacts performance and the ability to deploy more and more complex models. With a cutting-edge co-designed MLIR compiler and runtime architecture, and full control of the hardware, the compiler has access to traditionally unavailable features, that can be leveraged via novel compilation approaches to generate higher performance models.</p> <ul> <li>Take ownership of parts of AI Inference stack (Export/Compiler/Runtime) (flexible, based on skills/interests/needs) </li><li>Closely collaborate with AI team to guide them on the design and the development of Neural Networks into production </li><li>Collaborate with HW team to understand current HW architecture and propose future improvements </li><li>Develop algorithms to improve performance and reduce compiler overhead </li><li>Debug functional and performance issues on massively-parallel systems </li><li>Work on architecture-specific neural network optimization algorithms for high performance computing </li><li>Pursuing a degree in Computer Science, Computer Engineering, or relevant field of study with a graduation date between April 2026 -May 2027 </li><li>Strong C++ programming skills and familiarity with Python </li><li>Solid understanding of machine learning concepts and fundamentals </li><li>Capable of delivering results with minimal oversight </li><li>Experience with quantization, MLIR, CUDA, and LLMs is a huge plus </li></ul>