Machine Learning Engineer

Who we are 

Atoms is building the machines that power the next era of progress.

Over the last decade, software has transformed the digital world. But the physical world, where food is made, minerals are mined, goods are moved, and industries are run, remains far less intelligent, far less efficient, and far more constrained. We’re changing that.

Atoms builds 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 more than robotics. It 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.

What you’ll do

As an ML Intern on our team, you will help bridge the gap between high-level AI research and real-world physical actuation for our next-generation autonomous transport platforms. Working alongside senior engineers, you will contribute directly to production-grade models, real-time edge pipelines, and multi-sensor data systems that power our physical machines.

• Collaborate with research engineers to prototype, evaluate, and test emerging Machine Learning and Deep Learning models for trajectory planning and autonomous behavior.
• Help design and evaluate multimodal systems that integrate raw multi-sensor data (Cameras, LiDAR, Radar) to improve spatial-temporal perception.
• Assist in developing interactive world models and simulation tools to re-simulate real-world driving logs and analyze vehicle trajectories.
• Profile and optimize inference pipelines to help run complex models under low latency constraints on vehicle edge hardware.
• Work with data engineering workflows to identify, curate, and structure rare, complex edge cases and long-tail scenarios from physical operations.
• Partner with validation and QA teams to run model releases through simulated scenarios to catch performance regressions and track model behavior.

What we’re looking for

• Currently pursuing a BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, Data Science, or a related technical field.