Machine Learning Infrastructure Simulation Engineer, Optimus

The Optimus Simulation team is at the forefront of advancing humanoid robotics by building a high-fidelity virtual world where Optimus can safely learn, adapt, and improve. Our mission is to recreate the complexities of the real world in simulation, enabling scalable testing, rapid iteration, and accelerated development of the Optimus autonomy stack. We design and deploy cutting-edge simulation systems that blend accurate physics modeling, photorealistic rendering, and intelligent virtual agents, creating environments that challenge Optimus in the same way the physical world would. These simulations allow us to rigorously evaluate behavior, uncover edge cases, and drive continuous improvements in autonomy and decision-making.

As a member of this team, you will play a pivotal role in shaping the future of humanoid robotics. Your contributions will directly accelerate Optimus' ability to operate effectively in real-world environments by closing the gap between simulation and reality. We are looking for passionate engineers with expertise in distributed systems, ML infrastructure, and graphics / game development who are excited to build scalable RL infrastructure for training robots, pushing the boundaries of large-scale simulation, efficient training pipelines, and high-performance virtual environments.

What You'll Do Design, implement, and optimize scalable Simulation and RL infrastructure for training humanoid robots in simulated environments, leveraging distributed systems for parallel processing and high-throughput simulations

Optimize performance across the simulation stack, including distributed systems, Inference, and rendering, to ensure optimal usage of hardware resources and fast, efficient simulations

Drive innovation in sim-to-real strategies using simulation technologies and distributed computing to ensure Optimus performs reliably across both virtual and real-world environments

Deliver high-quality, production-ready code in a dynamic and fast-paced environment

What You'll Bring Hands-on experience building and scaling distributed systems for large-scale computations

Strong background in ML infrastructure, including designing training pipelines, data orchestration, and deployment of RL models at scale

Proficiency in GPU optimizations for either inference or rendering

Proficiency in Python, with familiarity in frameworks like PyTorch, TensorFlow, or RL libraries (e.g., Stable Baselines, RLlib), and a proven ability to write clean, scalable, and efficient code

Ability to research, implement, and adapt cutting-edge techniques from academic and industry sources into practical, production-ready solutions for scalable RL in simulation

Compensation and 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 : $132,000 - $390,000 / 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.

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