Machine Learning Engineer Intern Atoms · San Francisco, CA Internship · On-site $70 / hr 2 hours ago

Emploive; San Francisco (CA); On-site; USD 83,000 - 110,000; Full time

Job summary

Atoms in San Francisco seeks an ML Intern to bridge high-level AI research with real-world actuation for autonomous transport platforms.

You will work with senior engineers on production-grade models, real-time edge pipelines, and multi-sensor data systems that power our physical machines.

Qualifications

• Pursuing BS, MS, or PhD in a technical field such as CS, robotics, EE or data science.
• Strong foundation in deep learning and frameworks like PyTorch or JAX.
• Hands-on Python programming; familiarity with C++ is a plus.

Responsibilities

• Prototype, evaluate, and test ML/DL models for trajectory planning and autonomous behavior.
• Design multimodal systems integrating cameras, LiDAR, and Radar for perception.
• Develop world models and simulation tools to re-simulate real-world driving logs.
• Profile and optimize inference pipelines for low-latency edge hardware.
• Collaborate with data engineering to curate edge-case datasets from physical operations.
• Coordinate model releases through validation and QA simulations.

Skills

Deep learning basics

Python

C++

PyTorch/JAX

Data science

Education

BS in Computer Science

MS in CS/Robotics/EE/Data Science

PhD in CS/Robotics

Tools

PyTorch

JAX

ROS

Job description

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.
• Strong foundation in deep learning concepts and experience with modern frameworks like PyTorch or JAX (through coursework, research, or prior internships).
• Hands-on programming experience in Python; familiarity with C++ is a plus.
• Academic coursework, project experience, or research in one or more relevant domains: Computer Vision, Spatial-temporal Modeling, Reinforcement Learning, Model Optimization, Model Evaluation, or Data Engineering.
• Familiarity with robotics data structures or multi-sensor processing (Cameras, LiDAR, or Radar) is highly preferred.
• A passion for solving complex, messy real-world engineering problems and deploying AI into physical systems.

Why join us

At Atoms, you'll work on one of the defining challenges of our time- bringing automation into the physical world to drive real, lasting impact. We exist to uncover valuable unknown truths and turn them into progress, which means constantly pushing beyond what's known and building what doesn't yet exist. The work is ambitious and often challenging, but it’s grounded in a shared sense of purpose and a team committed to seeing it through together. Our work only matters if it serves others, and we know that meaningful progress depends on the trust of the people we serve and the strength of our team - so we invest in both, creating an environment where you can do your best work and grow.

What else you need to know

This role is based in our San Francisco office. Atoms is a company driven by invention and continuous change - we are constantly reimagining our industries, building new products, and refining how we operate. We do our best work together. That's why all of our office-based teams work onsite, five days a week.

Program Dates:

• Winter 2027: January - April
• Summer 2027: Late May - September

The base salary range for this role is $70.00 per hour.

Actual compensation will be determined on an individual basis and may vary depending on experience, skills, and qualifications.

Ready to join us as we serve those who serve others?

#LI-Onsite