Autonomy Perception Engineer - CV / 3D Reconstruction

Confirmed live in the last 24 hours

Zipline

South San Francisco, California, USA

Remote

Posted April 1, 2026

Job Description

About Zipline

Zipline is the world’s largest and most experienced drone delivery service. We are on a mission to serve all humans equally by ensuring access to food, medicine and essential goods anytime, anywhere. We design, build, and operate the world’s largest autonomous logistics system, delivering critical supplies quickly and reliably. Today, Zipline operates on four continents, makes a delivery somewhere in the world every 30 seconds, and has completed millions of deliveries to date, including blood, vaccines, medical supplies, food, and retail products.

Our customers include the world’s largest and most prominent healthcare systems, governments, retailers, restaurants and global businesses who rely on us to save lives, reduce emissions, increase economic opportunity, and provide delivery from point A to point B as fast as possible. The drone is only 15% of what we’ve built to enable seamless, reliable, global operations.

Our system strengthens supply chains, reduces congestion, and gives people time back. With more than 140 million commercial autonomous miles safely flown, Zipline is redefining access to healthcare, consumer products, and food across the globe.

We operate at a global scale and are looking for practical problem solvers who thrive on real-world challenges and rapid growth. Our team is motivated by building systems that have a direct, meaningful impact on people’s lives and by scaling the future of logistics. We are seeking people who sculpt from first principles, enjoy facing adversity, and can do the impossible at record breaking speeds.

About You and The Role

Zipline is operating the world’s largest autonomous logistics network—delivering critical medical and commercial goods globally with high reliability, precision, and scale. As we expand into increasingly complex, safety-critical environments, the systems behind our autonomy stack must be robust, adaptable, and deeply integrated—especially at the intersection of perception and deployment.

We're hiring senior and staff perception engineers to join our Droid team, the group responsible for the autonomy that powers Zipline’s backyard delivery experience. This team owns the full stack of offboard and cloud-side perception systems that inform, validate, and augment our onboard autonomy. From generating rich 3D and semantic priors from aerial survey data to learning customer preferences and terrain features at scale, your work will define how we prepare Zipline aircraft to perform mission-critical deliveries in complex, real-world environments.

This is not a research role—you’ll be expected to move fast, ship production-grade systems, and find clever ways to apply state-of-the-art techniques to tangible, high-impact problems.

What You’ll Do

Own the design and implementation of cloud-side autonomy pipelines that directly support and scale our onboard perception stack.
Leverage satellite imagery, aerial surveys, and structured data to build semantic and geometric world models of customer delivery zones.
Design and ship tools that predict deliverability, generate high-fidelity priors, and reduce the operational friction of onboarding new customers in new environments. You’ll step in where our on-vehicle capabilities can’t solve the problems we need to solve in order to scale the product.
Train and deploy mid- to large-scale models for semantic segmentation, 3D geometry, and learned preference modeling.
Design evaluation and validation infrastructure to ensure models behave reliably in the field.
Work across engineering to integrate your work into fleet-facing autonomy systems.
Lead architectural decisions, drive experimentation, and help the team push the limits of what’s possible with production-grade perception at scale.

What You'll Bring

At least 5+ years of experience building and deploying deep learning-based perception systems, particularly in 3D geometry, semantic understanding, or mapping from remote sensing data

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