{"id":2388627,"url":"https://alion.io/job/monarchlabs-computer-vision-engineer","title":"Computer Vision Engineer","company":{"id":4417470,"name":"Monarch","domain":"monarchlabs.org","url":"https://alion.io/company/monarchlabs","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Emeryville, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":143000,"max_usd":314000,"period":"year","method":"role_country_seniority_unknown","sample_n":4027},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"OpenCV","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false}],"status":"live","first_seen_at":"2026-09-05T18:32:48Z","employer_posted_date":"2026-09-05","last_verified_at":"2026-10-18T00:00:03Z","board_verified":true,"closed_at":null,"days_open":42,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":42},"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.\nFull-time, in-office in Emeryville, California.\nOur mosquito work\nTurn 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.\nKey Responsibilities\nDevelop and validate methods for detecting and tracking multiple mosquitoes in top-mounted behavioral-assay video\nDerive cumulative landing-zone occupancy, trajectories, spatial distribution, entry and exit rates, dwell time, and other interpretable behavioral features\nBuild representative labeled datasets and error analyses across labs, cameras, lighting conditions, arenas, mosquito densities, and occlusion patterns\nQuantify confidence and route uncertain or anomalous results to efficient human review rather than silently producing a score\nDesign visual overlays and quality-control tools that let scientists inspect how each measurement was produced\nWork with entomologists and lab teams to improve camera placement, assay geometry, capture standards, and the behavior labels that matter scientifically\nQualifications\nStrong experience with object detection, multi-object tracking, segmentation, pose or trajectory analysis, or related computer-vision methods\nStrong Python skills and experience with PyTorch, OpenCV, or equivalent tools\nExperience building evaluation sets and choosing metrics that reflect the downstream use of a vision system\nAbility to build efficient video-processing pipelines and debug failures at the frame and sequence level\nClear communication with domain scientists and software engineers\nDesired Attributes\nExperience with small-object tracking, animal behavior, microscopy, or other visually difficult scientific video\nExperience with domain adaptation, weak supervision, active learning, or human-in-the-loop annotation\nFamiliarity with camera calibration, experimental instrumentation, or cross-site capture standardization\nInterest in making scientific measurements interpretable and auditable\nOur crop-protection work\nTurn 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.\nKey Responsibilities\nDevelop and validate methods for detecting and tracking multiple insects in top-mounted behavioral-assay video\nDerive cumulative landing-zone occupancy, trajectories, spatial distribution, entry and exit rates, dwell time, and other interpretable behavioral features\nBuild representative labeled datasets and error analyses across labs, cameras, lighting conditions, crop surfaces, insect densities, and occlusion patterns\nQuantify confidence and route uncertain or anomalous results to efficient human review rather than silently producing a score\nDesign visual overlays and quality-control tools that let scientists inspect how each measurement was produced\nWork with entomologists and lab teams to improve camera placement, assay geometry, capture standards, and the behavior labels that matter scientifically\nQualifications\nStrong experience with object detection, multi-object tracking, segmentation, pose or trajectory analysis, or related computer-vision methods\nStrong Python skills and experience with PyTorch, OpenCV, or equivalent tools\nExperience building evaluation sets and choosing metrics that reflect the downstream use of a vision system\nAbility to build efficient video-processing pipelines and debug failures at the frame and sequence level\nClear communication with domain scientists and software engineers\nDesired Attributes\nExperience with small-object tracking, animal behavior, microscopy, or other visually difficult scientific video\nExperience with domain adaptation, weak supervision, active learning, or human-in-the-loop annotation\nFamiliarity with camera calibration, experimental instrumentation, or cross-site capture standardization\nInterest in making scientific measurements interpretable and auditable\nTo learn more, visit 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