AI Engineer, Multimodal Model Optimization, Autonomy & ...

What to ExpectScaling 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 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 DoDesign, train, and evaluate models optimized for edge acceleratorsFocus on improving model quality and training stability with model architecture designConduct scaling laws for model architecture and parallelism-aware compute efficiencyWork closely with AI engineers, compiler engineers, and hardware engineers to push the frontier of intelligence per wattDesign novel model architectures and algorithms for scaling and hardware utilizationInnovate in domains such as sparsity, distillation, quantization and parallelismProfile inference performance to ensure model architecture maximizes hardware efficiencyWhat You'll BringProven experience in scaling and optimizing large AI models, with a strong understanding of performance-related codesignProficiency in Python and a deep understanding of software engineering best practicesIn-depth knowledge of deep learning fundamentals, including optimization techniques, loss functions, and neural network architecturesExperience with deep learning frameworks such as PyTorch, TensorFlow, or JAXPractical experience leveraging GPUs, SIMD instructions, multithreading, or custom accelerators (e.g., TPUs, edge NPUs) for AI model inference and optimizationDeep understanding with bottlenecks of inference hardware - compute throughput, memory bandwidth, and interconnectSpecialized experience in one or more of the following domains: model architecture design, quantization-aware-training, model pruning, distillation, and ASIC architectureDemonstrated ability to work collaboratively in a cross-functional team environmentStrong problem-solving skills and the ability to troubleshoot complex system-level issuesCompensation and BenefitsBenefitsAlong 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 deductionFamily-building, fertility, adoption and surrogacy benefitsDental (including orthodontic coverage) and vision plans, both have options with a $0 paycheck contributionCompany Paid (Health Savings Account) HSA Contribution when enrolled in the High Deductible Aetna medical plan with HSAHealthcare and Dependent Care Flexible Spending Accounts (FSA)401(k) with employer match, Employee Stock Purchase Plans, and other financial benefitsCompany paid Basic Life, AD&D, short-term and long-term disability insuranceEmployee Assistance ProgramSick and Vacation time (Flex time for salary positions), and Paid HolidaysBack-up childcare and parenting support resourcesVoluntary benefits to include: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insuranceWeight Loss and Tobacco Cessation ProgramsTesla Babies programCommuter benefitsEmployee discounts and perks programExpected Compensation$124,000 - $420,000/annual salary + cash and stock awards + benefitsPay 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. #J-18808-Ljbffr

Created: 2025-09-17