Machine Learning Engineer - Action Models

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  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.

As a Senior Staff Machine Learning Engineer focused on Action Models, you will be one of the foundational technical leaders of Atoms' AI organization. You will help develop models that enable intelligent machines to reason about their environment, make decisions, and translate those decisions into actions in the physical world. This role sits at the intersection of machine learning, robotics, autonomous systems, planning, and embodied AI.

You will explore how modern foundation models, world models, and learned representations can be connected to action moving beyond systems built entirely from independently engineered components toward models capable of learning increasingly sophisticated behaviors from data and experience. The problems are open-ended, the architecture is still being defined, and the systems you build will ultimately need to work outside of a research environment on real machines operating in complex physical environments.

This is a deeply technical individual contributor role with significant influence over Atoms' research direction and long-term AI architecture.

• Define and help build Atoms' technical architecture for action models and learned decision-making systems.
• Develop models that translate learned representations of the physical world into decisions, plans, and actions.
• Explore architectures for reasoning, planning, control, and action generation within complex physical environments.
• Develop learned policies and action heads capable of operating across real-world robotics and autonomous systems.
• Explore approaches that connect perception and world models directly to downstream decision-making and control.
• Research and develop techniques across imitation learning, reinforcement learning, behavior learning, and other data-driven approaches to decision-making.
• Explore vision-language-action and other multimodal architectures for physical AI.
• Develop approaches that allow models to reason across temporal horizons and understand how actions influence future states.
• Train and evaluate models using large-scale real-world, simulated, and synthetic data.
• Develop methods for learning from demonstrations, human behavior, robot experience, and other sources of supervision.
• Make architectural decisions spanning data, model design, training, evaluation, inference, and deployment.
• Establish evaluation methodologies for measuring reasoning, planning, action quality, robustness, and generalization.
• Partner closely with researchers and engineers working across…