Autonomy Perception SWE

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 

At Zipline, we're shaping the future of safe airspace navigation and building the world’s largest autonomous delivery system. We're looking for senior machine learning and perception engineers to help define the state of the art in aircraft and obstacle detection and tracking — enabling our drones to safely and autonomously navigate complex, dynamic environments.

What You'll Do

• Advance the Frontiers of Perception: Design and deploy cutting-edge algorithms to detect, localize, and track aircraft and tall obstacles using monocular and multi-view camera systems — combining deep learning with geometric vision to achieve robust 3D situational awareness at scale.
• Engineer Reliable Vision Systems: Develop scalable model training pipelines and build rigorous evaluation frameworks that capture real-world performance across edge cases and long-tail scenarios.
• Integrate Across Systems: Collaborate with systems, flight test, and validation teams to design end-to-end test plans that push perception to the edge of capability — and beyond Zipline’s strict safety and reliability standards.
• Own Operational Performance: Create powerful debugging, visualization, and analysis tools that drive insight from field data, enabling rapid triage and root-cause analysis of perception issues in live deployments.
• Learn Fast, Iterate Faster: Leverage simulation and real-world feedback to continuously refine both ML models and classical tracking algorithms — improving accuracy, latency, and robustness in challenging operational conditions.
• Set the Standard for Airspace Autonomy: Contribute to the long-term vision and architecture of Zipline’s detect-and-avoid system, helping shape a perception stack that leads the industry in safety, reliability, and autonomy.

What You'll Bring

• 8+ years of professional experience crafting and deploying sophisticated perception systems for autonomous robots, aircraft, or vehicles.
• Expertise in state-of-the-art deep learning models, specifically focused on object detection and tracking, honed through real-world deployments.
• Deep knowledge of geometric computer vision techniques, including stereo vision, depth-from-motion, structure-from-motion, and visual odometry, for precise depth and 3D position estimation.
• Innovative mindset and practical problem-solving skills, demonstrated by a proven track record of overcoming performance bottlenecks and resolving operational edge cases.
• Hands-on experience supporting commercially operated autonomous systems, including diagnosing issues and enhancing system reliability in live environments.
• Exceptional communication and teamwork abilities, capable of thriving in a dynamic, cross-disciplinary engineering environment committed to safety-critical operations.

**What Else You Need To Know

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The starting cash range for this role is $180,000 - $265,000. Please note that this is a target, starting cash range for a candidate who meets the minimum qualifications for this role. The final cash pay for this role will depend on a variety of factors, including a specific candidate's experience, qualifications, skills, working location, and projected impact. The total compensation package for this role may also include: equity compensation; overtime pay; discretionary annual or performance bonuses; sales incentives; benefits such as medical, dental and vision insurance; paid time off; and more. 

Zipline is an equal opportunity employer and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws or our own sensibilities.

We value diversity at Zipline and welcome applications from those who are traditionally underrepresented in tech. If you like the sound of this position but are not sure if you are the perfect fit, please apply!

Voluntary Self-Identification

For government reporting purposes, we ask candidates to respond to the below self-identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.

As set forth in Zipline ’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.