Senior Computer Vision & Machine Learning Engineer

illumicell AI is developing a digital holographic microscope and cloud-based AI system for automated semen analysis. The Senior Computer Vision & Machine Learning Engineer will lead computer-vision capabilities from problem definition and evaluation through model development, integration, testing, and deployment, while contributing to device-to-cloud software and providing technical review and mentorship.

Responsibilities:

• Take technical responsibility for computer vision capabilities from problem definition and benchmark design through algorithm or model development, integration, and quantitative evaluation as a tested product component
• Develop, train, and deploy models for sperm detection, multi-object tracking, motility measurement, and image-quality assessment using PyTorch and production-grade frameworks
• Design evaluation systems with subject-disjoint dataset splits, versioned model artifacts, clinically relevant metrics, and error analysis that separates detector, tracker, reconstruction, and sample-quality failures
• Choose between classical image processing, deep learning, and combined approaches based on measured results rather than current trends
• Define annotation requirements, labeling specifications, and ground-truth standards; work with clinical collaborators and labeling partners to build and maintain training and test datasets
• Carry models through GPU profiling, batch inference optimization, code review, testing, and deployment, not just to a working prototype
• Contribute to device acquisition software, device-to-cloud data handling, cloud batch workers, APIs, and integration testing when those are the priority
• Establish repeatable training workflows: versioned configurations, experiment tracking, model provenance, and traceable performance results
• Use coding agents and engineering automation to accelerate implementation, testing, investigation, documentation, and traceability work, reviewing generated changes critically and verifying behavior before accepting them
• Review experiments and code from the ML team; work directly with a developing ML engineer and provide a reliable technical sounding board
• Investigate failures in dense clusters, cell crossings, debris, drift, blur, and reconstruction artifacts, and identify which part of the system is responsible
• Work from incomplete specifications: define a practical first step, identify the decisions that require input, and produce a result that can be reviewed and built on