Senior Staff ML Engineer - Action Models for Robotics AI

ATOMS Careers page; San Francisco (CA); On-site; USD 273,000 - 321,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Disability insurance

Life insurance

401(k)

Equity

Paid holidays

Parental leave

Pre-tax commuter benefits

Team lunch

Job summary

Atoms is shaping the future of Physical AI with robots deployed in food, mining, and transport. We seek a Senior Staff Machine Learning Engineer to lead action-model research, bridging ML, robotics, autonomy, and embodied AI in real-world machines.

You’ll influence architecture, train policies, and collaborate across perception, world models, simulation, and ML infra to build robust, scalable systems. Onsite in San Francisco.

Qualifications

• Deep expertise in machine learning for decision-making, robotics or embodied intelligence.
• Experience translating research ideas into usable ML systems.
• Strong software engineering and hands-on coding in Python/C++.

Responsibilities

• Define and help build architecture for action models and learned decision-making.
• Develop models translating learned representations into decisions, plans, actions.
• Explore architectures for reasoning, planning, control in complex physical environments.
• Train and evaluate models using real-world, simulated and synthetic data.
• Provide technical leadership through direction, reviews, and mentorship.

Skills

ML for decision-making

Robotics/embodied AI

Reinforcement learning

Imitation learning

Temporal modeling

Python/C++

System design

Education

PhD/MS in CS/ML

Tools

Python

C++

PyTorch

TensorFlow

Job description

Atoms is shaping the future of Physical AI with robots deployed in food, mining, and transport. We seek a Senior Staff Machine Learning Engineer to lead action-model research, bridging ML, robotics, autonomy, and embodied AI in real-world machines.

You’ll influence architecture, train policies, and collaborate across perception, world models, simulation, and ML infra to build robust, scalable systems. Onsite in San Francisco.