Staff Machine Learning Engineer - World Models City
Cssmerge · San Francisco, CA
About this role
**Staff Machine Learning Engineer — World Models (City)** **Who we are** Atoms is building the machines that power the next era of progress. Over the last decade, software transformed the digital world—but the physical world (food, mining, transport, and more) remains far less intelligent and far more constrained. We’re changing that. Atoms builds **Physical AI**: real-world robots for 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, scalable, and productive. This work requires deep integration across **hardware, software, AI, operations, manufacturing, and real estate**. We deploy into real environments, operate them, learn from them, and improve them until they work at scale. **About the role** As a **Senior Staff Machine Learning Engineer** focused on **World Models**, you’ll be a foundational technical leader within Atoms’ AI organization. You’ll help develop models that learn rich representations of the physical world from large-scale multimodal data—enabling machines to understand environments, model how they evolve, and provide representations for downstream reasoning and action. This is a deeply technical **individual contributor** role with significant influence over 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 that learn from **multimodal inputs** (video, images, spatial info, sensor data, robot state, and other real-world signals) - Explore architectures capturing **spatial, temporal, semantic, and physical relationships** - Develop approaches for learning how environments **evolve over time** and how **actions influence future states** - Research and build **self-supervised, generative, predictive, and representation learning** approaches for physical world intelligence - Explore modern foundation model architectures applied to **robotics and autonomous systems** - Develop training strategies leveraging large-scale **real and simulated datasets** - Make architectural decisions across **data, model design, pretraining, fine-tuning, evaluation, inference, and deployment** - Establish evaluation methodologies to measure a model’s ability to **understand, represent, and predict** the physical world - Partner closely with teams across **perception, action models, robotics, autonomy, simulation, and ML infrastructure** - Translate emerging research into systems that operate on **real machines in real environments** - Provide technical leadership via research direction, architecture reviews, mentorship, experimentation, and hands-on engineering - Help establish the technical bar for the growing AI Research organization and help identify exceptional talent **What we’re looking for** - Deep expertise in machine learning; experience building **large-scale deep learning** or **foundation model** systems - Strong understanding of modern model architectures and **representation learning** - Experience in one or more areas: **multimodal learning, video models, generative models, self-supervised learning, predictive models, spatial intelligence, embodied AI** - Experience training
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