Senior Software Engineer - Machine Learning Infrastructure

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

• Work on the Machine Learning and AI Infrastructure team, which is focused on providing infrastructure for data scientists and other engineers to use ML and AI technologies effectively.
• Build and maintain software and tools for classical ML stack which helps data scientists manage full training lifecycle from data collection, data preparation, training, deployment and model serving.
• Build and maintain software and tools which enable efficient and effective use of GenAI (LLM) technologies.
• Do background research on latest innovations in the Machine Learning space and bring back insights to apply within the team.

What we’re looking for

• 3+ years of experience as a software engineer, and the ability to work independently in a fast-paced, dynamic environment, taking initiative to drive results.
• Background in data infrastructure, platform infrastructure or ML infrastructure is preferred.
• Enthusiastic about learning new codebases, and making changes in large complex systems safely and efficiently.