At Tesla AI, we’re not just training models, we’re building the foundation models that power the future of real-world autonomy. Your work will directly control millions of Tesla vehicles and robotaxis on the road today, Optimus humanoid robots in factories and homes, and Digital Optimus — our groundbreaking AI agents that autonomously operate computers and software systems at enterprise scale. In addition, we deploy these models to edge hardware where efficiency and accuracy is paramount.

Whether your background is in pushing the limits of ultra-low-bit precision, developing post-training quantization and quantization-aware-training algorithms, or designing model architecture for quantized inference, if you excel at making massive deep learning models run lightning-fast on custom silicon and want to see your research deployed at planetary scale, this role is built for you.

At Tesla AI we celebrate and enable speed, ownership, and real-world impact. Join our AI Team and you’ll have: Unparalleled real-world data (tens of billions of miles of driving + robot interactions + digital workflows); One of the largest AI training clusters on Earth; Immediate closed-loop feedback from vehicles, physical robots, and real-time computer interfaces; The ability to ship improvements to millions of customers, robots, and digital agents in weeks, not years.

If you want your models to solve real physics, causality, long-horizon planning, dexterous manipulation, and autonomous digital task execution, this is the highest-leverage AI role on the planet. Let’s build the future together.

• Architect and scale quantization pipelines (both Post-Training Quantization and Quantization-Aware Training) for massive multi-modal foundation models that fuse vision, prediction, and decision-making. You will optimize inference latency, memory bandwidth utilization, and power consumption for self-driving cars, Optimus robots, and digital agents operating at enterprise scale
• Innovate quantization-aware-training recipes and algorithms that tackle complex optimization challenges inherent to low-precision training
• Push the limits of low-precision AI: Research and implement advanced low-bitweight post-training quantization techniques to address hard algorithmic problems such as activation outlier mitigation, KV cache compression, and optimal layer-wise bit-allocation while strictly maintaining model accuracy
• Collaborate closely with AI compiler, inference engine, and silicon teams to ensure models are architected to maximally utilize underlying hardware capabilities by co-designing quantization-friendly architectures, hardware-aware sparsity patterns, and mixed low-precision kernels
• Collaborate across perception, planning, robotics, digital agents, and infrastructure teams to move models from research to fleet-wide, robot-wide, and enterprise-wide deployment

• Degree or equivalent experience in Computer Science, Machine Learning, Robotics, Computer Vision, or related quantitative field
• 2+ years of hands-on experience training, optimizing, and deploying large-scale quantized deep learning models
• Strong technical understanding of the challenges inherent to quantizing large transformer architectures, including mitigating massive activation outliers, KV cache quantization, and maintaining the numerical stability of attention mechanisms at low precision
• Deep expertise in the theory and low-level implementation of modern quantization algorithms (e.g., GPTQ, AWQ, SmoothQuant, OmniQuant)
• Experience with low-level numerics and emerging data formats (e.g., FP8, INT4, W4A8, W8A8, micro-scaling/MX formats) and their trade-offs regarding latency, memory bandwidth, and model fidelity
• Rigorous understanding of computer architecture and the roofline model. Familiarity with how to optimize for memory hierarchies, minimize SRAM/DRAM data movement, and efficiently map quantized GEMMs and memory-bound operators to custom silicon
• Proficiency in writing custom CUDA/Triton kernels, implementing custom autograd functions (e.g., Straight-Through Estimators), and manipulating PyTorch computational graphs (e.g., FX tracing, torch.compile)
• Strong software engineering skills — clean, production-grade Python/C++ code that ships reliably at scale
• Proven ability to turn cutting-edge research into robust, real-world systems that improve safety, capability, efficiency, or digital productivity
• Passion for Tesla’s mission and excitement about deploying AI that moves both the physical and digital worlds forward

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

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.