MLInfrastructureEngineer

We are seeking a Machine Learning Engineer to work directly alongside our research scientists to train, evaluate, and deploy the models that make our robots move, perceive, and act in the real world. This is a hands-on ML role: you will train policies, debug convergence, run experiments in simulation, and push models onto hardware — not just build the pipes around them. You’ll bring deep expertise in reinforcement learning, computer vision, and supervised learning applied to robotics and embodied systems. You also need to think seriously about training infrastructure — managing GPU clusters, optimizing distributed training, and shipping models to edge devices — but the core of this role is getting in the loop with scientists and making models work. Key job responsibilities Train and iterate on neural network policies for locomotion, manipulation, navigation, and perception using reinforcement and supervised learning Design and run experiments in simulation (Isaac Lab, MuJoCo, or similar) and transfer results to physical hardware Debug training runs end-to-end: diagnosing convergence failures, reward shaping issues, data quality problems, and sim-to-real gaps Optimize models for deployment on edge hardware (NVIDIA Jetson) with strict latency and memory constraints Build and maintain MLOps infrastructure: experiment tracking, model versioning, evaluation pipelines, and reproducible training workflows About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the fu