Jobs at Monarch Crops

Founding Head of Talent responsible for full-cycle recruiting across science, engineering, operations, and global health roles. The role includes defining positions and interview processes, building talent pipelines, closing senior candidates, establishing recruiting infrastructure, and developing leveling, offer, and onboarding foundations. The leader will advise founders on talent strategy, organization design, and compensation while potentially building a recruiting team.

Leads distributed laboratory networks supporting standardized insect-behavior assays. Responsibilities include recruiting and onboarding partner labs, coordinating equipment, compounds, training, schedules, data submissions, troubleshooting, and follow-up experiments. Tracks capacity, turnaround, protocol adherence, controls, inter-lab agreement, and data quality while maintaining assay provenance and chain of custody. Develops onboarding and support systems, communicates recurring observations to science and operations teams, and travels to partner laboratories when remote support is insufficient.

Develop computer-vision methods for detecting and tracking insects in behavioral-assay videos. Build labeled datasets, evaluation metrics, video-processing pipelines, confidence estimates, visual overlays, and quality-control tools. Derive interpretable behavioral measurements and collaborate with entomologists, laboratory teams, and software engineers to improve imaging standards, annotations, and scientific reliability.

Conduct research on machine-learning methods that recommend informative scientific experiments. Develop multimodal models using molecular, assay, behavioral, and laboratory data; create active-learning and sequential experiment-selection methods; design evaluations robust to distribution shift; investigate generalizable behavioral signals; and translate model failures into improved experiments, labels, and controls. Communicate findings clearly to experimental scientists and validate models prospectively.

Build reliable machine learning and data systems that transform assay videos, scientific data, and experimental outcomes into reproducible models, evaluated predictions, and experiment recommendations. Responsibilities include data and feature pipelines, model training and evaluation infrastructure, batch and online inference systems, monitoring, data quality, versioning, observability, and interfaces across computer vision, molecular modeling, active learning, and laboratory workflows.