Staff Machine Learning Engineer - World Models City

About this role

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.

About the role

As a Senior Staff Machine Learning Engineer focused on World Models, you will be one of the foundational technical leaders of Atoms' AI organization. You will help develop models that learn rich representations of the physical world from large scale multimodal data enabling machines to understand environments, model how those environments evolve, and provide the learned representations needed for downstream reasoning and action. This is an opportunity to help define a new generation of physical AI systems.

Rather than relying exclusively on traditional, independently engineered perception and autonomy components, we are exploring foundation model approaches capable of learning from diverse sensor inputs and large amounts of real world experience. You will work at the intersection of foundation models, multimodal learning, computer vision, robotics, and embodied AI to help determine what these systems should look like at Atoms.