Senior Computer Vision & Machine Learning Engineer

illumicell AI is a medical-technology startup building AI-powered, label-free cell analysis to bring fast, objective diagnostics to the point of care. Based at the Pagliuca Harvard Life Lab, Harvard's startup incubator in Allston, we work at the intersection of optics, machine learning, and clinical medicine.

Starting with male fertility, we're developing a new standard for how living cells are measured, understood, and acted upon. We have a functional prototype device, a digital holographic scanner paired with cloud-based AI analysis that is already producing live-cell measurements, and we are rapidly accelerating toward FDA submission. We're backed by Accel and Tim Draper, are Techstars alumni, and are building the team that will carry the product from working prototype to cleared clinical device.

illumicell AI is building a digital holographic microscope for automated semen analysis. Our instrument captures live-cell video, reconstructs phase imagery in the cloud, and produces clinically relevant measurements (sperm concentration, motility, and eventually morphology) that fertility labs use to make treatment decisions.

We are looking for a Senior Computer Vision and Machine Learning Engineer to take technical responsibility for the path from difficult imaging problems to tested, production‑quality software. The problems are real and hard: dense fields of small moving cells, overlapping objects, reconstruction artifacts, limited ground truth, and the need to separate measurement error from sample and image‑quality failures. The work covers detection, multi‑object tracking, motion analysis, image quality, and evaluation design.

The role requires depth in image processing and computer vision, combined with enough software breadth to contribute to device acquisition, device‑to‑cloud data handling, cloud batch inference, and integration testing when those are the priority. The eventual product is intended to operate as regulated medical‑device software. You will join and complement an existing ML engineer who owns important detector, data, and productization work.

This role adds senior technical review, mentorship, and shared ownership. It is not a replacement role and does not include people management by default. Level may flex to Staff for a candidate who has demonstrated technical direction across multiple related systems.

• 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…