Staff Machine Learning Engineer - World Models City

Job description

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.

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

What you'll do

• Define and help build Atoms' technical architecture for world models and foundation models for physical AI.

• Develop large scale models that learn representations of complex, dynamic physical environments.

• Build models capable of learning from multimodal inputs including video, images, spatial information, sensor data, robot state, and other realworld signals.

• Explore architectures that capture spatial, temporal, semantic, and physical relationships within realworld environments.

• Develop approaches for learning how environments evolve over time and how actions influence future states.

• Research and build self supervised, generative, predictive, and representation earning approaches for physical world intelligence.

• Explore the application of modern foundation model architectures to robotics and autonomous systems.

• Develop training strategies that take advantage of largescale realworld and simulated datasets.

• Make architectural decisions spanning data, model design, pretraining, finetuning, evaluation, inference, and deployment.

• Establish evaluation methodologies for measuring a model's ability to understand, represent, and predict the physical world.

• Partner closely with engineers and researchers working across perception, action models, robotics, autonomy, simulation, and ML infrastructure.

• Translate emerging research into systems capable of operating on real machines in real environments.

• Provide technical leadership through research direction, architecture reviews, mentorship, experimentation, and handson engineering.

• Help establish the technical bar for the growing AI Research organization and participate in identifying and assessing exceptional engineering and research talent.

What we're looking for

• Deep expertise in machine learning with experience developing large-scale deep learning or foundation model systems.

• Strong understanding of modern model architectures and representation learning.

• Experience with one or more areas such as multimodal learning, video models, generative models, self-supervised learning, predictive models, spatial intelligence, or embodied AI.

• Experience training models on large-scale datasets and understanding the relationship between data, architecture, compute, and model performance.

• Strong understanding of the full ML lifecycle, including data strategy, model architecture, training, evaluation, optimization, and inference.

• Experience translating research ideas into functioning machine learning systems.

• Strong software engineering fundamentals and the ability to remain deeply handson in Python

Prepare for the interview

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