Staff Machine Learning Infrastructure Engineer

Who we are Atoms is building the machines that power the next era of progress. Over the last decade, software has transformed the digital world. But the physical world, where food is made, minerals are mined, goods are moved, and industries are run, remains far less intelligent, far less efficient, and far more constrained. We’re changing that. Atoms builds Physical AI— real-world robots for the industries that move civilization forward, starting with food, mining, and transport. Our systems are designed to understand, predict, and control the real world with precision, turning complex physical operations into something more reliable, more scalable, and more productive. This work requires more than robotics. It requires deep integration across hardware, software, AI, operations, manufacturing, and real estate. We don’t just build machines in a lab. We deploy them into real environments, operate them, learn from them, and improve them until they work at scale. We are roboticists, engineers, operators, and builders. We believe the next great technology companies will not only transform information, but the physical systems that shape everyday life. If you want to work on hard problems with real-world impact, join us. What you’ll do We are seeking a foundational Staff Machine Learning Infrastructure Engineer to design and build the large-scale ML training infrastructure that powers our next-generation autonomous transport models. In this role, you will design the high-performance training pipelines and validation environments that enable our world-class robotics and ML researchers to iterate rapidly. You will own the challenge of scaling distributed GPU workloads to support a high volume of concurrent training runs across an expanding vehicle fleet, building a platform that can flexibly run on whatever GPU capacity is available, regardless of provider or environment, directly accelerating innovation across the platform. Training Infrastructure: Design, implement, and scale repeatable machine learning infrastructure utilizing Kubernetes to support large-scale distributed GPU training of novel neural networks. Distributed Computing & Orchestration: Leverage distributed compute frameworks to efficiently manage and execute a high volume of complex ML training jobs concurrently across large GPU clusters. Experiment Tracking & MLOps: Integrate advanced model management and experiment tracking tools to provide researchers with deep observability into training metrics and run performance. Data Engineering Pipelines: Build and optimize high-throughput data ingestion pipelines to seamlessly stream petabyte-scale multi-sensor vehicle logs into training environments. Validation at Scale: Architect robust infrastructure for autonomous model validation and continuous integration testing, ensuring new vehicle policy releases are entirely regression-free. Cross-Functional Collaboration: Partner closely with core robotics engineers and…