Staff Machine Learning Infrastructure Engineer

Atoms is building the machines that power the next era of progress. They are seeking a foundational Machine Learning Infrastructure Engineer to design and build the large-scale ML training infrastructure that powers their next-generation autonomous transport models.

Responsibilities:

• Design, implement, and scale repeatable machine learning infrastructure utilizing Kubernetes to support large-scale distributed GPU training of novel neural networks
• Leverage distributed compute frameworks to efficiently manage and execute a high volume of complex ML training jobs concurrently across large GPU clusters
• Integrate advanced model management and experiment tracking tools to provide researchers with deep observability into training metrics and run performance
• Build and optimize high-throughput data ingestion pipelines to seamlessly stream petabyte-scale multi-sensor vehicle logs into training environments
• Architect robust infrastructure for autonomous model validation and continuous integration testing, ensuring new vehicle policy releases are entirely regression-free
• Partner closely with core robotics engineers and machine learning researchers to eliminate workflow bottlenecks and accelerate the deploy-to-vehicle lifecycle