Computer Vision Engineer

Monarch; Emeryville (CA); On-site; USD 120,000 - 170,000; Full time

Job summary

Monarch in Emeryville, CA, is hiring for a full-time in-office role focused on developing and validating methods to detect and track mosquitoes and other insects in top-mounted behavioral assays. You will derive behavioral metrics such as occupancy, trajectories, and dwell times, and build robust labeled datasets across labs and conditions.

You will work with entomologists and lab teams to optimize camera setup, assay geometry, and measurement labels while ensuring transparent, auditable

Qualifications

• 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.

Responsibilities

• 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.

Skills

Object detection

Multi-object tracking

Segmentation

Pose/trajectory analysis

Python

PyTorch

OpenCV

Clear communication

Tools

PyTorch

OpenCV

Job description

We offer opportunities to do your life’s work while helping solve one of the most important technical and moral challenges of our time.

Full-time, in-office in Emeryville, California.

Our mosquito work

Turn raw assay video into precise, reviewable measurements of what mosquitoes do over time. The work begins with detection and tracking, but the scientific outcome is a trustworthy behavioral record that can train and evaluate models.

Key Responsibilities

• 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

Qualifications

• 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

Desired Attributes

• 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

Our crop-protection work

Turn raw assay video into precise, reviewable measurements of what insects do over time. The work begins with detection and tracking, but the scientific outcome is a trustworthy behavioral record that can train and evaluate models.

Key Responsibilities

• 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

Qualifications

• 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

Desired Attributes

• 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