Tesla

Makes electric cars, robotaxis and grid batteries

Machine Learning Infrastructure Simulation Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-timePalo Alto, CA, USANo salary listedIn-person

About the Job

Requirements

Hands-on experience building and scaling distributed systems for large-scale computations.
A strong background in machine learning infrastructure, including designing training pipelines, data orchestration, and deployment of reinforcement learning models at scale.
Proficiency in GPU optimization for inference or rendering.
Proficiency in Python and familiarity with frameworks such as PyTorch, TensorFlow, or reinforcement learning libraries such as Stable Baselines and RLlib; 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 reinforcement learning in simulation.

Responsibilities

Design, implement, and optimize scalable simulation and reinforcement learning 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 hardware resource usage and fast, efficient simulations.
Drive innovation in sim-to-real strategies using simulation technologies and distributed computing to ensure Optimus performs reliably in virtual and real-world environments.
Deliver high-quality, production-ready code in a dynamic and fast-paced environment.

Category	Tesla SI
Location	Palo Alto, California
Requisition ID	252374
Type	Full-time

Job Description

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.<br><br>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.

Job Responsibilities

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

Job Requirements

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 familairty 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

About the Company

Tesla

Makes electric cars, robotaxis and grid batteries

Tesla makes electric cars and batteries that store power for homes and utilities, and it develops self-driving software and humanoid robots. In 2023, its Model Y became the first electric vehicle to rank as the world's best-selling car. Buyers order the Model 3 sedan, Model Y SUV, or Cybertruck pickup directly from Tesla instead of through dealers, and they charge on its Supercharger fast-charging network. It also sells Megapack and Powerwall batteries, runs a robotaxi service, and charges drivers a monthly fee for self-driving software that still needs their supervision. Tesla was founded in 2003, with Elon Musk as its lead early investor and a recognized co-founder, and it is based in Austin, Texas.

Company size: 10,001+
Company stage: IPO
Headquarters: Austin, TX
Founded: 2003

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