As a Senior Machine Learning Engineer specializing in medical imaging within the Data Impact & Governance department, you will help shape the future of clinical AI by building, deploying, and operating imaging models that directly impact patient care. This role offers the unique opportunity to work at the cutting edge of applied medical imaging ML within a world‑renowned cancer center—where your solutions influence diagnosis, treatment, safety, and operational excellence.

What’s in it for you?

• Exceptional Benefits: MD Anderson provides paid medical benefits, generous PTO, and strong retirement plans, supporting your health, well‑being, and long-term financial security.
• High‑Impact Work: Your models will be used in real clinical workflows—helping clinicians detect disease, streamline operations, and support better outcomes for patients.
• Advanced Technical Environment: Work with large-scale imaging datasets, enterprise GPU infrastructure, distributed compute, and cutting‑edge ML technologies—all within a governed clinical environment.
• Career Growth & Visibility: Collaborate closely with clinicians, data scientists, ML leadership, radiologists, and operational teams. Your work will influence institutional AI strategy and governance.
• Innovation with Responsibility: Help advance safe, ethical, and trustworthy AI practices in one of the world’s leading cancer centers.
• Collaborative Culture: Be part of a mission-driven organization that values innovation, learning, and teamwork.

Summary

The Senior Machine Learning Engineer – Medical Imaging owns the full lifecycle of clinical computer vision models deployed across the enterprise. This includes defining clinical ML problems, designing and training models, conducting rigorous validation, deploying models into clinical environments, and ensuring ongoing performance and reliability in real-world workflows.

The role is intended for engineers experienced in deploying and operating medical imaging ML models in production—especially within regulated, clinical, or safety‑sensitive settings. You will collaborate with multidisciplinary teams, investigate model performance issues such as distribution shift or protocol variability, and ensure responsible AI adoption through strong documentation, traceability, and governance alignment.

Major Work Activities

Core 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.

Competencies

Technical Expertise

• Experience developing, deploying, and operating medical imaging ML models in regulated clinical environments.
• Ability to build imaging data pipelines involving DICOM workflows, dataset versioning, and distributed training.
• 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.

Analytical Expertise

• 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.
• Ability to evaluate model performance quantitatively in the context of clinical workflows and operational realities.
• Experience engaging clinicians, patient safety, and business stakeholders to communicate model performance, impacts, and risk considerations.
• Ability to assess model generalizability and failure modes across scanners, sites, and populations.

Oral & Written Communication

• Collaborate effectively with data scientists, ML engineers, software teams, clinicians, and operational leaders to integrate imaging models into real workflows.
• Produce clear, comprehensive technical documentation including design specs, validation reports, and runbooks.
• Communicate project risks, timelines, and outcomes to leadership and governance bodies.
• Contribute to internal technical standards, best practices, and shared ML development frameworks.
• Present technical and non‑technical updates clearly across multiple stakeholder groups.