AI Performance Engineer

What To Expect

The Performance Optimization team at Tesla takes research models and makes them run efficiently on Tesla's AI-ASICs, powering Autopilot and Optimus. This AI Performance Engineer 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.

What You'll Do

• Implement, optimize, and profile highly performant kernels for inference and training on Tesla's AI and Dojo ASICs
• Optimize bottlenecks in the inference flow, make precision/performance tradeoff decisions, and develop novel techniques to improve hardware utilization and throughput
• 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
• Experiment with numerical methods and alternative architectures
• Collaborate with the compiler team on programmability and performance

What You'll Bring

• 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
• Degree in Engineering, Computer Science, or equivalent in experience and evidence of exceptional ability
• Contributions to ML serving frameworks, compilers, and frameworks is a bonus (e.g. SGLang, LLVM, PyTorch, MLIR)

Benefits

Along with competitive pay, as a full-time Tesla employee, you are eligible for the following benefits at day 1 of hire:

• Medical plans > plan options with $0 payroll deduction
• Family-building, fertility, adoption and surrogacy benefits
• Dental (including orthodontic coverage) and vision plans, both have options with a $0 paycheck contribution
• Company Paid (Health Savings Accounts) HSA Contribution when enrolled in the High-Deductible medical plan with HSA
• Healthcare and Dependent Care Flexible Spending Accounts (FSA)
• 401(k) with employer match, Employee Stock Purchase Plans, and other financial benefits
• Company paid Basic Life, AD&D
• Short-term and long-term disability insurance (90 day waiting period)
• Employee Assistance Program
• Sick and Vacation time (Flex time for salary positions, Accrued hours for Hourly positions), 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
• Weight Loss and Tobacco Cessation Programs
• Tesla Babies program
• Commuter benefits
• Employee discounts and perks program

Expected Compensation

The expected compensation for this AI Performance Engineer role ranges from $132,000 - $330,000 annually, plus cash and stock awards and comprehensive benefits. 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.

Key skills/competency

• Kernel Optimization
• AI-ASIC Performance
• Compiler Development (MLIR)
• Hardware Co-design
• Machine Learning Inference
• Distributed Systems
• Performance Modeling
• GPU/ASIC Architecture
• Quantization Techniques
• Low-Latency Systems