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Tesla

Industry:
Humanoid Robotics
Location:
Job Type:
Software
AI Infrastructure Engineer, Digital Optimus
Job Description:

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.

Job 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
Job 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
Apply on Tesla Website
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