Machine Learning Infrastructure Engineer

- 5+ years of non-internship professional software development experience
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team
- Bachelor's degree or above in robotics, mechanical/mechatronics engineering, systems engineering or related field
- Knowledge of data structures, algorithm design, statistics, and system design
- Experience leading the design, build and deployment of complex and performant (reliable and scalable) software solutions in production
- Experience facilitating discussions with senior leadership regarding technical / architectural trade-offs, best practices, and risk mitigation
- Experience in robotics design, automation systems development, control systems design, or related product development
- Experience with training and deploying machine learning systems to solve large-scale optimizations, or experience in development or technical support
- Experience mentoring or training the engineering community on complex technical issues
- Track record of delivering developer-facing products with robust SDKs and fault-tolerant distributed systems.

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USA, NY, New York - 184,900.00 - 250,200.00 USD annually
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.