Tesla Internship Software Engineer AI Inference Winter/Spring 

United States, California, Palo Alto 

173317061

Consider before submitting an application:

This position is expected to start around January 2025 and continue through the entire Winter term (i.e. through May 2025) or into Summer 2025 if available. We ask for a minimum of 12 weeks, full-time and on-site, for most 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

Our team productionalizes ML models - we train and deploy large neural networks for efficient inference on compute-constrained edge devices (CPU / GPU / AI ASIC). The nature of this role is multi-disciplinary - you will work at the intersection of machine learning and systems by building the ML frameworks and infrastructure that enable the seamless training, deployment, and inference of all neural networks that run on Autopilot and Optimus.

• Build robust AI frameworks to lower neural networks to edge devices
• Build robust AI infrastructure to train and fine-tune networks for Autopilot and Optimus on large GPU clusters
• Deploy state-of-the-art neural networks on heterogenous compute, including Tesla’s in-house AI ASIC, with an aim to maximize network performance while minimizing latency
• Collaborate with AI scientists and compiler engineers to effectively compress large models to run in low precision
• Design and implement custom GPU kernels (CUDA / OpenCL) for efficient training and post-processing of network outputs

• Pursuing a degree in Computer Science, Computer Engineering, or relevant field of study with a graduation date between 2025 -2026
• Must be able to relocate and work on site in Palo Alto, CA
• Proficiency with Python and C++, including modern C++ (14/17/20)
• Proficiency with PyTorch or another machine learning framework
• Proficiency with training and deploying neural networks for real-world AI
• Proficiency with computer systems and computer architecture
• Experience with CUDA