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# Computer Vision Engineer

- **Company:** Monarch
- **Location:** Emeryville, CA, United States
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Software Debugging, Python (Programming Language), Object Detection, OpenCV, Pytorch, Data Pipelines
- **Published:** September 13, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=34ecb0abafe4e84d

## About the Role

* Strong experience with object detection, multi-object tracking, segmentation, pose or trajectory analysis, or related computer-vision methods
 * Strong Python skills and experience with PyTorch, OpenCV, or equivalent tools
 * Experience building evaluation sets and choosing metrics that reflect the downstream use of a vision system
 * Ability to build efficient video-processing pipelines and debug failures at the frame and sequence level
 * Clear communication with domain scientists and software engineers, * Experience with small-object tracking, animal behavior, microscopy, or other visually difficult scientific video
 * Experience with domain adaptation, weak supervision, active learning, or human-in-the-loop annotation
 * Familiarity with camera calibration, experimental instrumentation, or cross-site capture standardization
 * Interest in making scientific measurements interpretable and auditable

## Description

* Develop and validate methods for detecting and tracking multiple mosquitoes in top-mounted behavioral-assay video
 * Derive cumulative landing-zone occupancy, trajectories, spatial distribution, entry and exit rates, dwell time, and other interpretable behavioral features
 * Build representative labeled datasets and error analyses across labs, cameras, lighting conditions, arenas, mosquito densities, and occlusion patterns
 * Quantify confidence and route uncertain or anomalous results to efficient human review rather than silently producing a score
 * Design visual overlays and quality-control tools that let scientists inspect how each measurement was produced
 * Work with entomologists and lab teams to improve camera placement, assay geometry, capture standards, and the behavior labels that matter scientifically, * Develop and validate methods for detecting and tracking multiple insects in top-mounted behavioral-assay video
 * Derive cumulative landing-zone occupancy, trajectories, spatial distribution, entry and exit rates, dwell time, and other interpretable behavioral features
 * Build representative labeled datasets and error analyses across labs, cameras, lighting conditions, crop surfaces, insect densities, and occlusion patterns
 * Quantify confidence and route uncertain or anomalous results to efficient human review rather than silently producing a score
 * Design visual overlays and quality-control tools that let scientists inspect how each measurement was produced
 * Work with entomologists and lab teams to improve camera placement, assay geometry, capture standards, and the behavior labels that matter scientifically


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