Senior Perception 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

We are seeking a Perception Engineer to develop the systems that enable our autonomous machines to understand and interact with the physical world.

You will work on perception problems at the intersection of computer vision, machine learning, robotics, and autonomous systems, building models and systems that transform real-world sensor data into reliable representations of the environment.

Depending on your background and area of expertise, you may:

• Design, train, and deploy machine learning models for perception in autonomous systems.
• Develop algorithms for object detection, classification, segmentation, tracking, and scene understanding.
• Build systems for 3D perception and geometric reasoning using cameras, LiDAR, radar, or other sensors.
• Develop approaches for multi-sensor and multi-modal fusion, combining complementary sensor information into robust representations of the environment.
• Build temporal models that reason about objects, motion, trajectories, and changes in the environment over time.
• Improve perception performance across challenging real-world conditions, including occlusion, sensor noise, changing environments, and edge cases.
• Develop data, training, evaluation, and experimentation pipelines that accelerate perception development.
• Define metrics and evaluation methodologies that connect offline model performance to real-world system behavior.
• Optimize perception models and systems for deployment on resource-constrained or latency-sensitive robotic platforms.
• Investigate failures observed in deployed systems and translate those findings into improvements across models, data, and architecture.
• Collaborate closely with AI Research, Robotics, ML Infrastructure, Software, Hardware, and Systems Engineering teams.

 

What we're looking for

• 3-5 years of relevant experience working with perception systems and computer vision & machine learning models.
• Strong software engineering skills in Python and/or C++.
• Experience developing perception systems for autonomous vehicles, robotics, drones, industrial automation, or other embodied systems.
• Experience with camera, LiDAR, radar, IMU, or other robotic sensor modalities.
• Experience with multi-modal or multi-sensor architectures.
• Knowledge of 3D geometry, coordinate transformations, calibration, localization, or state estimation.
• Experience deploying ML models into real-time or safety-critical systems.
• Experience optimizing models for latency, compute, memory, or power constraints.
• Experience working with large-scale perception datasets and training infrastructure.
• Experience diagnosing long-tail failures and improving production perception systems through data-driven iteration.
• Experience taking research ideas from experimentation through deployment on physical systems.
• Publications or significant research contributions in computer vision, machine learning, robotics, or related fields are a plus, but not required.

 

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.

The base salary range for this role is $208,000 - $245,000 per year.

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

Base salary is just one part of your total rewards package. You may also be eligible for equity awards.

Benefits Summary (USA Full-Time Exempt Employees):

• 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

Benefits are subject to change at the company's discretion.
Atoms accepts applications on an ongoing basis.

 

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

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