SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
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 and operate machines at scale in real environments.
As a Staff Machine Learning Engineer focused on World Models, you will be a foundational technical leader in Atoms' AI organization. You will 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 learned 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.
Key responsibilities include:
- 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 real-world signals
- Explore architectures that capture spatial, temporal, semantic, and physical relationships within real-world 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 learning approaches for physical world intelligence
- Explore the application of modern foundation model architectures to robotics and autonomous systems
- Develop training strategies that leverage large-scale real-world and simulated datasets
- Make architectural decisions spanning data, model design, pretraining, fine-tuning, 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 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 hands-on engineering
- Help establish the technical bar for the growing AI Research organization and participate in identifying and assessing exceptional talent
The role is based in San Francisco with five-day-per-week on-site work required.
Requirements:
- 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 hands-on in Python and modern ML frameworks
- 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
