AI Infrastructure Engineer, Digital Optimus

Job description

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