Scaling transformers, as well as more recent advances in Reinforcement Learning with Verifiable Rewards (RLVR), has created models with Ph-D level intelligence in a wide variety of subject areas - from Math to Social Sciences. Yet these models continue to struggle in real-world physical reasoning, often struggling to tell left from right.
At Tesla AI, we want to develop Olympiad-level physical intelligence that will enable highly capable robots, both wheeled and legged. These models should be able to anticipate and reason about future movements of any object or scene at the level of a race car driver or professional athlete. To accomplish this, you will have access to petabytes of multimodal (video, audio, action etc.) real-world data from our global fleet of cars and robots, as well as Tesla's state-of-the-art compute resources.
In this role, you will have the opportunity to work on the datasets, infra, model architecture, eval and scaling laws necessary to pretrain a large multimodal model with an emphasis on real-world physical intelligence.
What You'll Do
Design, train, and evaluate models optimized for edge accelerators
Focus on improving model quality and training stability with model architecture design
Conduct scaling laws for model architecture and parallelism-aware compute efficiency
Work closely with AI engineers, compiler engineers, and hardware engineers to push the frontier of intelligence per watt
Design novel model architectures and algorithms for scaling and hardware utilization
Innovate in domains such as sparsity, distillation, quantization and parallelism
Profile inference performance to ensure model architecture maximizes hardware efficiency
What You'll Bring
Proven experience in scaling and optimizing large AI models, with a strong understanding of performance-related codesign
Proficiency in Python and a deep understanding of software engineering best practices
In-depth knowledge of deep learning fundamentals, including optimization techniques, loss functions, and neural network architectures
Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX
Practical experience leveraging GPUs, SIMD instructions, multithreading, or custom accelerators (e.g., TPUs, edge NPUs) for AI model inference and optimization
Deep understanding with bottlenecks of inference hardware - compute throughput, memory bandwidth, and interconnect
Specialized experience in one or more of the following domains: model architecture design, quantization-aware-training, model pruning, distillation, and ASIC architecture
Demonstrated ability to work collaboratively in a cross-functional team environment
Strong problem-solving skills and the ability to troubleshoot complex system-level issues
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:
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 options with a $0 paycheck contribution
Company Paid (Health Savings Account) HSA Contribution when enrolled in the High Deductible Aetna 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
Employee Assistance Program
Sick and Vacation time (Flex time for salary 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 $124,000 - $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.