Staff Machine Learning Engineer - Action Models

Engg; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

Atoms is building AI-powered robotic systems for the real world, combining ML, robotics, and embedded systems. You will help design and implement action models and decision-making architectures for intelligent machines operating in complex environments.

You will work across perception, world models, and ML infrastructure, driving research direction and hands-on engineering in a highly collaborative setting.

Qualifications

• Deep expertise in ML for decision-making, robotics or embodied intelligence.
• Strong understanding of modern deep learning architectures and sequential decision-making.
• Experience with RL, imitation learning, planning, control, or embodied AI.
• Experience linking learned representations to downstream actions.
• Hands-on in Python and/or C++.

Responsibilities

• Define and help build Atoms' architecture for action models and learned decision-making systems.
• Develop models translating learned representations into decisions, plans, and actions.
• Explore architectures for reasoning, planning, control, and action generation.
• Develop policies suitable for real-world robotics and autonomous systems.
• Connect perception and world models to decision-making and control.
• Research imitation learning, reinforcement learning, and behavior learning approaches.
• Train and evaluate models on real-world, simulated, and synthetic data.
• Provide technical leadership through architecture reviews and mentorship.

Skills

Machine learning

Decision-making models

Robotics

Autonomous systems

Embodied intelligence

Python

C++

Deep learning architectures

Sequential modeling

Tools

Python

C++

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 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.

What you'll do

• 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 perception, world models, robotics, autonomy, simulation, and ML infrastructure.
• Translate emerging research in embodied intelligence into systems capable of operating reliably on real machines.
• Provide technical leadership through research direction, architecture reviews, mentorship, experimentation, and hands‑on 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 models for decision-making, robotics, autonomous systems, or embodied intelligence.
• Strong understanding of modern deep learning architectures and their application to sequential decision-making and physical systems.
• Experience with one or more areas such as reinforcement learning, imitation learning, behavior learning, planning, control, robot learning, or embodied AI.
• Experience developing systems that connect learned representations or perception to downstream actions.
• Strong understanding of sequential and temporal modeling and the relationship between actions and future states.
• Experience training and evaluating models using large-scale real-world, simulated, or synthetic datasets.
• 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 hands‑on in Python and/or C++.
• Demonstrated ability to operate in ambiguous research spaces where the architecture and solution may not yet be known.
• A track record of making consequential technical decisions and influencing research or engineering direction beyond an individual project.
• Ability to communicate complex research and technical ideas clearly and collaborate across research, engineering, and robotics disciplines.

Why join us

At Atoms, you’ll work on one of the defining challenges of our time—bringing automation into the physical world to drive real, lasting impact. We exist to uncover valuable unknown truths and turn them into pro