Autonomy Perception CV/ML SWE

Zipline•South San Francisco, CA

10h

About The Position

Zipline is the world’s largest and most experienced drone delivery service, on a mission to serve all humans equally by ensuring access to food, medicine and essential goods anytime, anywhere. They design, build, and operate the world’s largest autonomous logistics system, delivering critical supplies quickly and reliably. Zipline operates on four continents, makes a delivery every 30 seconds, and has completed millions of deliveries including blood, vaccines, medical supplies, food, and retail products. Their system strengthens supply chains, reduces congestion, and gives people time back, having safely flown over 140 million commercial autonomous miles. The company seeks practical problem solvers who thrive on real-world challenges and rapid growth, motivated by building systems with a direct, meaningful impact on people’s lives and scaling the future of logistics. This role is for senior machine learning and perception engineers to help define the state of the art in aircraft and obstacle detection and tracking, enabling drones to safely and autonomously navigate complex, dynamic environments.

Requirements

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.

Responsibilities

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.