ML/RL Infra EngineerDigital 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.ResponsibilitiesDesign & implement scalable distributed training infrastructure for large agentic models, supporting imitation learning, reinforcement learning (online & offline), and long-horizon training workflowsBuild high-fidelity, ultra-realistic training & simulation environments capable of handling complex, interruptible, long-context agent trajectories at massive scaleOptimize ML and RL training pipelines for throughput, cost-efficiency, and reliability across multi-node GPU clustersImplement advanced model serving, quantization, distillation, and deployment strategies tailored for Tesla's hardware platformsCollaborate with research, AI engineering, and production teams to productionize agent systems and integrate them with Tesla's autonomy (FSD) and robotics (Optimus) platformsDesign systems for efficient context management, checkpointing, and orchestration of long-horizon agentic workloadsContinuously improve developer velocity through better tooling, CI/CD for ML, experiment tracking, and reproducible training environmentsRequirementsExperience in ML infrastructure, large-scale distributed systems, or high-performance computing for deep learning/reinforcement learningStrong expertise with training frameworks (PyTorch, JAX, DeepSpeed, FSDP, Megatron, etc.) and distributed training at scaleDeep knowledge of GPU/accelerator optimization, model parallelism, quantization, and edge deploymentProficiency in Python, Kubernetes, cloud infrastructure (or on-prem clusters), and modern MLOps practicesExperience building data pipelines and simulation environments for reinforcement learning or robotics applications is highly valuedStrong software engineering fundamentals, system design skills, and a passion for building reliable, observable, and high-performance ML platformsAbility to work effectively in a fast-paced, cross-functional environment with researchers and engineersCompensation & BenefitsBenefitsAlong 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 deductionFamily-building, fertility, adoption and surrogacy benefitsDental (including orthodontic coverage) and vision plans, both have options with a $0 paycheck contributionCompany Paid (Health Savings Accounts) HSA Contribution when enrolled in the High-Deductible 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&DShort-term and long-term disability insurance (90 day waiting period)Employee Assistance ProgramSick and Vacation time (Flex time for salary positions, Accrued hours for Hourly 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 $140,000 - $252,000/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.
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