Autonomy Zip Perception SWE

Zipline is building the world's largest autonomous delivery system, and this role focuses on advancing the perception stack for safe airspace navigation. As a senior machine learning and perception engineer, you will design and deploy algorithms to detect, localize, and track aircraft and obstacles using camera systems, develop scalable training pipelines, and integrate with systems and flight test teams. You will own operational performance through debugging and analysis tools, and contribute to the long-term vision of the detect-and-avoid system.

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.

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.