Job SummaryHologic is seeking a Software Algorithm Engineer 4 to design, train and validate deep learning models for cytology on the Genius™ Digital Diagnostics System. This role owns the cell-level CNN classifier, nuclear-to-cytoplasmic (NC) ratio segmentation pipeline, and the technical evidence needed to support FDA and other regulatory submissions. The engineer serves as the technical lead for model design and verification under design controls, working closely with cytopathologists, imaging scientists, regulatory and quality teams.
Key Responsibilities- Design, train and tune multi-class CNNs to classify cells and aggregate predictions into specimen-level diagnostic categories.
- Develop multi-task models with shared encoders and feature heads for diagnostic class, cytomorphologic features and NC ratio category.
- Build nucleus and cytoplasm segmentation networks to measure NC ratio and nuclear area, including overlapping cells in clusters.
- Own the data pipeline, including patient-grouped splits, color profile application, class balance and imaging metadata.
- Run disciplined experiments, benchmark lightweight backbones against larger reference models and define evaluation metrics and reports.
- Collaborate with pathologists on labeling protocols, inter-observer agreement and adjudication processes.
- Prepare design documentation, verification/validation protocols and technical content for regulatory submissions.
- Mentor engineers and establish coding, reproducibility and MLOps standards.
- Ensure model performance and interpretability, including Grad-CAM checks and feature outputs that align with pathologist expectations.
- Support regulatory strategy for AI-enabled medical devices under design controls and PCCP guidance.
QualificationsEducation- MS or PhD in computer science, biomedical engineering, electrical engineering or a related field, or BS with equivalent experience.
Experience- 7+ years of software development, including 4+ years building and deploying deep learning models for computer vision.
- Coaching and mentorship experience a plus
Skills- Expert Python and PyTorch (or TensorFlow), including CNN fine-tuning, transfer learning and segmentation (e.g., U-Net, segmentation_models_pytorch).
- Experience with imbalanced, multi-class and multi-task problems, and metrics such as per-class sensitivity, F1, kappa and AUROC.
- Experience with medical or microscopy imaging, including color handling, stain variation and high-resolution tiling.
- Strong discipline in data splitting, leakage prevention, experiment tracking and reproducible training.
- Clear written communication for technical reports and design documentation.
- Preferred: digital pathology or cytology AI experience (ideally urine cytology); experience with FDA 510(k), De Novo, PMA or EU IVDR; familiarity with IEC 62304, ISO 13485, ISO 14971, 21 CFR Part 820 and FDA PCCP guidance.
- Preferred: ICC color management, stain deconvolution and scanner color calibration; cell instance segmentation tools (StarDist, Cellpose) and annotation tools (QuPath, CVAT, napari).
- Preferred: model optimization for edge or embedded inference (quantization, ONNX, TensorRT) and experience leading or technically guiding a small team.
The annualized base salary range for this role is $133,000 - $190,000 and is bonus eligible. Final compensation packages will ultimately depend on factors including relevant experience, skillset, knowledge, education, business needs and market demand.
Agency and Third-Party Recruiter NoticeAgencies that submit a resume to Hologic must have a current executed Hologic Agency Agreement executed by a member of the Human Resource Department. In addition, Agencies may only submit candidates to positions for which they have been invited to do so by a Hologic Recruiter. All resumes must be sent to the Hologic Recruiter under these terms or they will not be considered.
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