Komen Graduate Training Program UT MDACC · 10 hours ago
Senior Machine Learning Engineer - Medical Imaging
United States
Full-time
Remote
Senior Level
$146K/yr - $220K/yr
5+ years expKomen Graduate Training Program UT MDACC is a world-renowned cancer center seeking a Senior Machine Learning Engineer specializing in medical imaging. The role involves building, deploying, and operating imaging models that directly impact patient care, collaborating with multidisciplinary teams to ensure the responsible adoption of AI technologies.
Hospital & Health Care
No H1B
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
Qualification
Required
Bachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline
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
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
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
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
Preferred
Master's Degree or PHD with a concentration in Science, Engineering, or related field
Experience operating medical imaging ML systems across multiple sites, scanners, or protocols, rather than a single controlled environment
Experience handling post-deployment failures, including performance degradation, clinical incidents, model updates, or corrective actions
Experience 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
Paid medical benefits
Generous PTO
Strong retirement plans
Tuition benefits
Educational opportunities
Individual and team recognition
Company
Komen Graduate Training Program UT MDACC
Through generous funding from Susan G.
11-50 employees
Funding
Current Stage
Early StageCompany data provided by crunchbase