ML Infrastructure Engineer, Fauna

Amazon; New York (NY); On-site; USD 100,000 - 150,000; Full time

Job summary

Amazon is seeking a Machine Learning Engineer at Fauna Robotics to work alongside research scientists. This role involves training and deploying models for robotic functions like locomotion and perception.

The ideal candidate will have expertise in reinforcement learning and computer vision, experience with GPU infrastructure, and skills in building MLOps systems. You'll play a key part in solving complex challenges in robotics to make systems more accessible and interactive.

Qualifications

• Deep expertise in reinforcement learning and robotics.
• Experience in managing GPU infrastructure and optimizing distributed training.
• Ability to troubleshoot data quality issues.

Responsibilities

• Train and iterate on neural network policies.
• Design and run experiments in simulation and on hardware.
• Debug and optimize models for deployment.

Skills

Reinforcement learning

Computer vision

Supervised learning

GPU cluster management

MLOps

Tools

NVIDIA Jetson

Isaac Lab

MuJoCo

Job description

Description

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 full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful.

At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build.

If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.