Senior Machine Learning Engineer

Hologic • Newark, DE

8h • Remote

About The Position

As a Senior Machine Learning Engineer within Hologic’s Breast & Skeletal Health division, you will design, develop, and deploy advanced AI algorithms for next‑generation medical imaging devices. A key focus of this role is building and validating AI‑driven solutions for breast cancer detection in breast tomosynthesis (3D mammography). Your work will directly impact patient outcomes by ensuring our AI solutions are accurate, robust, safe, and clinically validated, supporting Hologic’s mission to improve women’s health through innovative, high‑quality technologies.

Requirements

• 5+ years of hands‑on experience in machine learning, applied AI, or ML engineering, ideally with exposure to computer vision or medical imaging.
• Bachelor’s degree in a related field preferred (e.g., Computer Science, Electrical/Computer Engineering, Data Science, Mathematics, Statistics, Biomedical Engineering, or similar).
• Strong understanding of machine learning and deep learning principles, including supervised learning and, ideally, self‑supervised or semi‑supervised methods.
• Solid knowledge of neural network architectures and training techniques, particularly for computer vision (e.g., CNNs, modern vision architectures).
• Foundation in computer vision techniques, data preprocessing, feature engineering, and statistical analysis.
• Experience with model validation and performance benchmarking, including selecting appropriate metrics and designing experiments.
• Strong programming skills in Python (required); C++ experience is a strong plus.
• Proficiency with ML/data science libraries and tools such as NumPy, SciPy, Pandas, OpenCV, scikit‑learn, XGBoost.
• Hands‑on experience with deep learning frameworks such as PyTorch and/or TensorFlow.
• Experience designing and implementing scalable ML pipelines, including training, inference, and monitoring.
• Experience with cloud platforms (e.g., AWS, Azure, or GCP) for training and/or deploying ML models.
• Familiarity with software engineering best practices, including modular design, testing, debugging, version control (e.g., Git), and CI/CD concepts.

Nice To Haves

• Advanced degree (Master’s or PhD) is a plus but not required.
• Familiarity with the DICOM format and medical imaging workflows, ideally including digital breast tomosynthesis (DBT).
• Understanding of breast cancer pathology, radiology workflows, and how mammography is used in clinical practice.
• Experience working under FDA regulatory standards or similar frameworks for AI/ML in healthcare, including validation protocols, documentation, and risk management.
• Experience contributing to regulated medical device software or products subject to 510(k)/PMA submissions.
• Exposure to MLOps practices and tools (e.g., MLflow, Kubeflow, SageMaker, model monitoring, experiment tracking).
• Ability to translate complex technical concepts into clear language for clinical, product, and business stakeholders.
• Strong problem‑solving skills and a practical mindset focused on delivering reliable, real‑world solutions.
• Demonstrated ability to work independently while also collaborating effectively in cross‑functional teams.
• Experience mentoring or guiding junior team members and contributing to team standards and best practices.
• High level of ownership, attention to detail, and commitment to quality, especially given the impact on patient care.

Responsibilities

• Design and develop ML/AI models for breast imaging, with a focus on digital breast tomosynthesis (DBT) and related modalities.
• Own the end‑to-end ML pipeline: data preparation, feature engineering, model training, evaluation, deployment, and monitoring in production environments.
• Work with large‑scale medical imaging datasets, including DICOM images, to build high‑performing and reliable models.
• Implement and optimize deep learning architectures (e.g., CNNs and related vision models) for detection, classification, and related tasks in breast imaging.
• Validate and benchmark models using appropriate metrics, cross‑validation, test sets, and clinically relevant performance measures.
• Collaborate with cross‑functional teams (engineering, clinical, regulatory, and product) to translate clinical needs and business requirements into robust ML solutions.
• Contribute to design and documentation needed for regulatory submissions and clinical validation, following FDA and other relevant guidelines for AI in healthcare.
• Ensure code quality and reliability, applying good software engineering practices (testing, version control, code reviews, documentation).
• Mentor junior engineers and scientists, sharing best practices in ML, deep learning, and software development.

Benefits

• We offer a competitive salary and annual bonus scheme
• comprehensive training when you join as well as continued development and training throughout your career.

What This Job Offers

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