Impress employers and recruiters.
Choose from hundreds of resume examples.
Impress employers and recruiters.
Choose from hundreds of resume examples.
Tailor your resume to this Staff Machine Learning Engineer - World Models City role.
Rezi rewrites your resume against ATOMS Careers page's job description. Free.
Tailor your resume to this Staff Machine Learning Engineer - World Models City role.
Rezi rewrites your resume against ATOMS Careers page's job description. Free.
Don't guess if your resume is good enough.
See how it scores against the Staff Machine Learning Engineer - World Models City posting at ATOMS Careers page — free, in seconds.
Don't guess if your resume is good enough.
See how it scores against the Staff Machine Learning Engineer - World Models City posting at ATOMS Careers page — free, in seconds.
About the Role
As a Senior Staff Machine Learning Engineer focused on World Models, you will be a foundational technical leader in the 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 their evolution, and provide representations for downstream reasoning and action. This role offers the opportunity to define a new generation of physical AI systems by exploring foundation model approaches capable of learning from diverse sensor inputs and real-world experience.
Responsibilities
- 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 take advantage of large-scale real-world and simulated datasets.
- Make architectural decisions spanning data, model design, pretraining, finetuning, 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 working 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 engineering and research talent.
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.
Skills
- Machine Learning
- Foundation Models
- Multimodal Learning
- Video Models
- Generative Models
- Self-Supervised Learning
- Predictive Models
- Spatial Intelligence
- Embodied AI
- Python
- ML Frameworks
Location
- San Francisco
Work Type
- Onsite
Experience Level
- Senior Staff
Salary/Compensations
- $273,000 - $321,000 per year
Benefits
- Medical, Dental, Vision, Disability, and Life Insurance
- Flexible Spending Account / Health Savings Account Options
- 401(k)
- Equity
- Sick Time, Unlimited Flexible Time Off, and Paid Holidays
- Paid Parental Leave
- Pre-Tax Commuter Benefit Plan
- Team lunch in our SoMa office every Tuesday and Thursday
About the Company
- Atoms is building the machines that power the next era of progress.
- We build 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 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.