Principal Software Algorithm Engineer - Digital Cytology AI

Hologic • Marlborough, MA

1d • $157,000 - $220,000

About The Position

Hologic is seeking a Principal Software Algorithm Engineer to design, train, and validate deep learning models for cytology on the Genius™ Digital Diagnostics System. The role owns the cell-level CNN classifier, nuclear-to-cytoplasmic (NC) ratio segmentation pipeline, and technical evidence supporting FDA and other regulatory submissions. The engineer serves as technical lead for model design and verification under design controls, working closely with cytopathologists, imaging scientists, and regulatory and quality teams.

The position includes developing cell-classification and segmentation models, managing imaging data and experiments, collaborating on labeling and evaluation, preparing design and regulatory documentation, and establishing engineering and MLOps practices. It also supports regulatory strategy for AI-enabled medical devices under design controls and PCCP guidance.

Requirements

• MS or PhD in computer science, biomedical engineering, electrical engineering, or a related field, or a BS with equivalent experience.
• 10+ years of software development, including 5+ years building and deploying deep learning models for computer vision.
• Demonstrated leadership experience, including setting strategic direction, influencing senior stakeholders, leading cross-functional initiatives, and driving accountability and execution in a complex, matrixed organization.
• Expertise in Python and PyTorch or TensorFlow, including CNN fine-tuning, transfer learning, and segmentation.
• 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.

Nice To Haves

• 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.
• ICC color management, stain deconvolution, and scanner color calibration.
• Cell instance segmentation tools such as StarDist or Cellpose, and annotation tools such as QuPath, CVAT, or napari.
• Model optimization for edge or embedded inference, including quantization, ONNX, or TensorRT.
• Experience leading or technically guiding a small team.

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 for 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 and 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 aligned with pathologist expectations.
• Support regulatory strategy for AI-enabled medical devices under design controls and PCCP guidance.

Benefits

• Comprehensive training upon joining and continued development and training throughout the career.
• Annualized base salary range of $157,000–$220,000; bonus eligible.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal