Staff Machine Learning Engineer - Action Models
Cssmerge · San Francisco, CA
About this role
**Who we are** Atoms is building the machines that power the next era of progress—real-world robots for industries that move civilization forward (starting with food, mining, and transport). We design systems 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 (Action Models)**, you’ll be a foundational technical leader in Atoms’ AI organization. You’ll 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 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 in complex physical environments - Develop learned policies and action heads for real-world robotics and autonomous systems - Explore approaches connecting 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 decision-making approaches - Explore vision-language-action and other multimodal architectures for physical AI - Develop approaches that 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 supervision sources - Make architectural decisions across data, model design, training, evaluation, inference, and deployment - Establish evaluation methodologies for reasoning, planning, action quality, robustness, and generalization - Partner closely across perception, world models, robotics, autonomy, simulation, and ML infrastructure - Translate embodied intelligence research into systems that operate reliably on real machines - 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, with experience building models for decision-making, robotics, autonomous systems, or embodied intelligence - Strong understanding of modern deep learning architectures for sequential decision-making and physical systems - Experience in one or more areas: reinforcement learning, imitation learning, behavior learning, planning, control, robot learning, or embodied AI - Experience building systems that connect learned representations/perception to downstream actions - Strong understanding of sequential and temporal modeling, and the relationship between actions and future states - Experience trai
Listing freshness
CronJobs last confirmed this listing 1h ago. If its source stops confirming the opening for seven days, this page is removed from active inventory.