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# machine learning and perception engineers

- **Company:** Zipline
- **Location:** South San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $180,000.0 - $265,000.0
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Software Debugging, Machine Learning, Object Detection, Deep Learning, Reliability of Systems, Low Latency
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/autonomy-zip-perception-swe-zipline-9604947

## About the Role

* 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.

## Description

* 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.


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