Senior Machine Learning Engineer - Medical Imaging

About the role

The Senior Machine Learning Engineer - Medical Imaging specializes in medical imaging within the Data Impact & Governance department. This role involves shaping the future of clinical AI by building, deploying, and operating imaging models that directly impact patient care. The position offers the unique opportunity to work at the cutting edge of applied medical imaging ML within a world-renowned cancer center where solutions influence diagnosis, treatment, safety, and operational excellence.

Responsibilities

• Own the full lifecycle of medical imaging ML models-from problem definition and model development to deployment, monitoring, maintenance, and retirement.
• Participate as a technical owner in formal governance, release, and incident review processes, with clear escalation paths and responsibilities.
• Translate clinical imaging use cases into deployable AI solutions with defined evaluation metrics, operating thresholds, and reproducible implementation strategies.
• Design and execute post-deployment monitoring, including detection and mitigation of model degradation due to distribution shift, scanner changes, or labeling variability.
• Collaborate with ML platform, data science, IT, and clinical operations teams to deploy and operate models in secure enterprise environments.
• Maintain responsible AI practices, ensuring traceability of data, models, experiments, and documentation of limitations and failure modes.
• Contribute to fallback, rollback, and model decommissioning strategies to support patient safety and operational continuity.
• Engage clinical, technical, and operational partners to support safe adoption and communicate model risks, behaviors, and performance.
• Mentor junior team members and contribute to best practices, review standards, and reproducible ML workflows.

Requirements

Experience developing, deploying, and operating medical imaging ML models in regulated clinical environments.

Deep proficiency in Python and PyTorch for model training and inference under GPU and memory constraints.

Experience orchestrating ML workflows using Airflow, Prefect, or similar DAG-based systems.

Skilled in deploying containerized ML workloads on enterprise cloud platforms such as Azure using Kubernetes.

Understanding of audit-ready model tracking, lineage, and controlled promotion workflows.

Ability to scope medical imaging ML projects end to end, considering clinical and regulatory constraints.

Experience designing validation strategies aligned with governance, regulatory expectations, and change control processes.

Knowledge of healthcare data privacy requirements as they relate to medical imaging and clinical metadata.

Experience engaging clinicians, patient safety, and business stakeholders to communicate model performance, impacts, and risk considerations.

Experience assessing model generalizability and failure modes across scanners, sites, and populations.

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline.
• Preferred: Master's Degree or PhD with a concentration in Science, Engineering, or related field.
• Required: Five years of experience in machine learning engineering, data science, data engineering, and/or software engineering. With Master's degree, three years' experience required. With PhD, one year of experience required.

Skills

• Technical expertise in medical imaging ML, including DICOM workflows, dataset versioning, and distributed training.
• Strong analytical skills, including the ability to evaluate model performance in clinical workflows and operational realities.
• Effective communication skills, including the ability to collaborate with data scientists, ML engineers, software teams, clinicians, and operational leaders to integrate imaging models into real workflows.
• Experience in deploying and operating medical imaging ML systems across multiple sites, scanners, or protocols.
• Experience handling post-deployment failures, including performance degradation, clinical incidents, model updates, or corrective actions.
• Experience in raising the technical bar for team members, such as establishing reproducibility practices, review standards, or shared patterns.
• Experience technically evaluating third-party medical imaging AI within clinical workflows.

Benefits

Exceptional Benefits: MD Anderson provides paid medical benefits, generous PTO, and strong retirement plans, supporting your health, well-being, and long-term financial security.