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# AI Training Infrastructure Engineer

- **Company:** AI ROBOTICS LLC
- **Location:** San Jose, CA, United States
- **Salary:** $150,000.0
- **Contract:** Permanent contract
- **Skills:** Distributed Systems, Job Scheduling, Python (Programming Language), Machine Learning, Software Systems, Reinforcement Learning, Pytorch, Machine Learning Operations, Data Pipelines
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/ai-training-infrastructure-engineer-humanoid-whole-body-control-figure-2-7937572

## About the Role

* Strong software engineering fundamentals with production experience in Python and PyTorch
 * Experience building or scaling training infrastructure for robotics, control systems, or large-scale ML workloads
 * Familiarity with physics simulation tools such as NVIDIA PhysX, MuJoCo, Warp, or PyBullet
 * Working knowledge of dynamics, controls, and robotics systems
 * Experience with reinforcement learning, imitation learning, or policy distillation
 * Strong ownership mindset-you own systems that your teammates rely on every day
 * Experience modeling contact interactions and photorealistic simulation environments for complex manipulation tasks

Bonus Qualifications:

 * Experience with humanoid or legged robot control
 * Background in distributed systems, job schedulers, or cluster management
 * Experience deploying ML models or control policies to real-world systems

## Description

We're looking for an engineer to own the training and deployment backbone behind our RL-based whole-body control systems. This role sits at the intersection of robotics, machine learning, controls, and software systems engineering, and is critical to how quickly we can iterate, train, and deploy new capability to our fleet of humanoid robots., * Own and scale the infrastructure used to train whole-body control policies (simulation, data pipelines, orchestration, visualizations)

 * Design systems that are fast, reliable, and highly configurable for our controls engineers
 * Ensure high cluster utilization and minimal downtime-unblocking the team and accelerating iteration cycles
 * Evaluate and integrate physics engines, simulation environments, and parameterizations to balance realism and training speed
 * Optimize hyperparameters and infrastructure to maximize training speed and efficiency and final model performance
 * Build robust tooling to take policies from training * validation * deployment on hardware


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