Job Description

What to Expect

Our team designs, trains, and deploys large-scale neural networks optimized for inference on compute-constrained edge devices (CPU / GPU / custom AI ASIC). This role sits at the intersection of ML modeling and hardware-aware systems engineering — you will architect and train state-of-the-art models while co-designing them with the underlying silicon and compiler stack to maximize performance. You will drive the full lifecycle from model research and training at scale to quantized, latency-optimized deployment across Tesla's heterogeneous compute platforms.

What You’ll Do

• Design, train, and iterate on neural network architectures for autonomous driving and robotics, with a focus on efficiency-aware model design (architecture search, distillation, pruning, quantization-aware training)
• Co-design model architectures with compiler and ASIC teams to exploit hardware-specific capabilities (custom ops, dataflow patterns, memory hierarchy)
• Develop and optimize the model lowering and deployment pipeline from PyTorch to edge inference on Tesla's in-house AI ASIC
• Profile and optimize end-to-end inference latency and throughput across heterogeneous compute targets
• Implement custom CUDA / GPU kernels for training, post-processing, or operations not natively supported by frameworks
• Collaborate with AI teams on translating modeling breakthroughs into production-ready, hardware-efficient implementations

What You’ll Bring

• Strong foundation in deep learning, hands-on experience designing, training, and debugging neural network architectures (transformers, convnets, diffusion models, etc.)
• Proficiency with PyTorch (or equivalent framework), including distributed training, custom autograd ops, and mixed-precision workflows
• Proficiency with Python and C/C++ (modern C++17/20 preferred)
• Solid understanding of computer architecture and systems concepts (memory hierarchy, instruction pipelines, accelerator design)
• Experience with model optimization techniques: quantization, pruning, knowledge distillation, or neural architecture search
• Experience with CUDA or GPU kernel development
• Familiarity with ML compiler stacks or model lowering toolchains (e.g., TVM, XLA, MLIR, TensorRT) is a plus

Compensation and Benefits

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

$176,000 - $420,000/annual salary + cash and stock awards + 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.