AI Infrastructure Engineer, Digital Optimus

ML/RL Infra Engineer Digital Optimus is the software counterpart to our physical humanoid, designed to interact with computer interfaces and perform long-horizon agentic behaviors. Our approach is modeled after real-time control policies rather than screenshot-based VLM agents, with the larger goal of integrating with Tesla's broader AI ecosystem. We're seeking an ML/RL Infra Engineer to build scalable, reliable infrastructure that powers these agents and enables seamless, high-volume rollouts for model evaluation & RL training. Top candidates will have deep experience in large-scale ML systems, high-performance training, and edge deployment, though evidence of exceptional ability matters more than relevance alone.

Responsibilities Design & implement scalable distributed training infrastructure for large agentic models, supporting imitation learning, reinforcement learning (online & offline), and long-horizon training workflows

Build high-fidelity, ultra-realistic training & simulation environments capable of handling complex, interruptible, long-context agent trajectories at massive scale

Optimize ML and RL training pipelines for throughput, cost-efficiency, and reliability across multi-node GPU clusters

Implement advanced model serving, quantization, distillation, and deployment strategies tailored for Tesla's hardware platforms

Collaborate with research, AI engineering, and production teams to productionize agent systems and integrate them with Tesla's autonomy (FSD) and robotics (Optimus) platforms

Design systems for efficient context management, checkpointing, and orchestration of long-horizon agentic workloads

Continuously improve developer velocity through better tooling, CI/CD for ML, experiment tracking, and reproducible training environments

Requirements Experience in ML infrastructure, large-scale distributed systems, or high-performance computing for deep learning/reinforcement learning

Strong expertise with training frameworks (PyTorch, JAX, DeepSpeed, FSDP, Megatron, etc.) and distributed training at scale

Deep knowledge of GPU/accelerator optimization, model parallelism, quantization, and edge deployment

Proficiency in Python, Kubernetes, cloud infrastructure (or on-prem clusters), and modern MLOps practices

Experience building data pipelines and simulation environments for reinforcement learning or robotics applications is highly valued

Strong software engineering fundamentals, system design skills, and a passion for building reliable, observable, and high-performance ML platforms

Ability to work effectively in a fast-paced, cross-functional environment with researchers and engineers

Compensation & Benefits Benefits

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

Expected Compensation $140,000 - $252,000/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.