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Salary: USD 273,000 - 321,000 / annual
Atoms is building Physical AI—real-world robots for industries including food, mining, and transport. The company integrates hardware, software, AI, operations, manufacturing, and real estate to deploy intelligent machines into real environments at scale.
As a Staff Machine Learning Engineer focused on Action Models, you will be a foundational technical leader in Atoms' AI organization. This is a deeply technical individual contributor role with significant influence over the company's research direction and long-term AI architecture.
You will develop models that enable intelligent machines to reason about their environment, make decisions, and translate those decisions into actions in the physical world. The work sits at the intersection of machine learning, robotics, autonomous systems, planning, and embodied AI. You'll explore how modern foundation models, world models, and learned representations can be connected to action, moving beyond independently engineered components toward models capable of learning sophisticated behaviors from data and experience.
Key responsibilities include:
- 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
- Research and develop techniques across imitation learning, reinforcement learning, behavior learning, and other data-driven approaches
- Explore vision-language-action and other multimodal architectures for physical AI
- 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 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 exceptional talent
The role is based in San Francisco with five-day-per-week onsite work required. 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.
REQUIREMENTS:
- 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
