Internship, AI Performance Engineer, Performance Optimization (Fall 2026)

Consider before submitting an application: This position is expected to start August or September 2026 and continue through summer term (ending approximately December 2026) or continuing into Winter/Spring 2027 if available and there is an opportunity to do so. We ask for a minimum of 12 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 Performance Optimization team takes research models and makes them run efficiently on Tesla's AI-ASICs, powering Autopilot and Optimus. This role sits at the intersection of AI research, compiler development, kernel optimization, and hardware design. You'll collaborate with AI researchers on novel quantization techniques, precision improvements, and non-standard architectures. You'll develop optimized kernels using our MLIR-based compiler stack to achieve real-time latency for self-driving and humanoid robots. You'll work directly with hardware teams to shape our next-generation AI and Dojo chips.

Responsibilities

Implement, optimize, and profile highly performant kernels for inference and training on Tesla's AI and Dojo ASICs Work on a variety of edge and datacenter workloads, from small encoders/decoders to distributed LLM inference Work with hardware teams to shape the next generation of Tesla hardware, evaluating architectural tradeoffs and balancing performance with versatility Research and implement state-of-the-art machine learning techniques to achieve high performance on our hardware Collaborate with the compiler team on programmability and performance

Requirements

Pursuing a degree in Computer Science, Machine Learning, Computer Engineering, or relevant field of study with a graduation date between December 2026 - December 2027 Hands-on experience with kernel optimization, distributed systems, inference runtimes, or serving frameworks Experience with LLMs, transformers, state space models, diffusion models, CNNs, and their performance characteristics Familiarity with performance modeling and roofline analysis Understanding of computer and GPU architecture, SIMD, multithreading, and/or other accelerators with vectorized instructions Strong analytical and debugging skills Ability to work across team boundaries with compiler, hardware, and ML teams Bonus: contributions to ML serving frameworks, compilers, and frameworks (e.g. SGLang, LLVM, PyTorch, MLIR)