Internship, Software Engineer, AI Inference Co-design (Winter/Spring 2026)

Consider before submitting an application:

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.

The AI inference co-design team's goal is to take research models and make them run efficiently on our AI-ASIC to power real-time inference for Autopilot and Optimus programs. This unique role lies at the intersection of AI research, compiler development, kernel optimization, math and HW design. You will work extensively with AI engineers and come up with novel techniques to quantize models, improve precision and explore non-standard alternate architectures. You will be developing optimized micro kernels using a cutting-edge MLIR compiler and solve the performance bottlenecks needed to achieve real-time latency needed for self-driving and humanoid robots. You will work closely with the HW team and bring state-of-the-art HW architecture techniques to our next generation HW SoCs.

• Research and implement state-of-the-art machine learning techniques to achieve high performance on our edge hardware
  
• Optimize bottlenecks in the inference flow, make precision/performance tradeoff decisions and figure out novel techniques to improve hardware utilization and throughput
  
• Implement/improve highly performant micro kernels for Tesla's AI ASIC
  
• Work with AI teams to design edge friendly neural network architectures
  
• Collect extensive performance benchmarks (latency, throughput, power) and work with HW teams to shape the next generation of inference hardware, balancing performance with versatility
  
• Experiment with numerical methods and alternative architectures
  
• Collaborate with the compiler infrastructure for programmability and performance
  

• Pursuing a degree in Engineering, Computer Science or similar with a graduation date between 2026-2027
• Experience with AI networks, such as CNNs, transformers, and diffusion model architectures, and their performance characteristics
• Understanding of GPU, SIMD, multithreading and/or other accelerators with vectorized instructions
• Exposure to computer architecture and chip architecture/micro-architecture
• Experience in one or more of the following machine learning/deep learning domains: Model compression, hardware aware model optimizations, hardware accelerators architecture, GPU/ASIC architecture, machine learning compilers, high performance computing, performance optimizations, numerics and SW/HW co-design

As a full-time Tesla Intern, you will be eligible for:

• Aetna PPO and HSA plans > 2 medical plan options with $0 payroll deduction
• Family-building, fertility, adoption and surrogacy benefits
• Dental (including orthodontic coverage) and vision plans. Both have an option with a $0 payroll contribution
• Company Paid (Health Savings Account) HSA Contribution when enrolled in the High Deductible Medical Plan with HSA
• Healthcare and Dependent Care Flexible Spending Accounts (FSA)
• 401(k), Employee Stock Purchase Plans, and other financial benefits
• Company Paid Basic Life, AD&D, and short-term disability insurance
• Employee Assistance Program
• Sick time after 90 days of employment and Paid Holidays
• Back-up childcare and parenting support resources
• Voluntary benefits to include: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance
• Commuter benefits
• Employee discounts and perks program

Pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.