[Remote] Senior Machine Learning Engineer - Medical Imaging

Note: The job is a remote job and is open to candidates in USA. The University of Texas MD Anderson Cancer Center is a world-renowned institution specializing in cancer care, and they are seeking a Senior Machine Learning Engineer specializing in medical imaging. This role involves owning the full lifecycle of clinical computer vision models, collaborating with multidisciplinary teams, and ensuring responsible AI adoption in clinical workflows.ResponsibilitiesOwn the full lifecycle of medical imaging ML models—from problem definition and model development to deployment, monitoring, maintenance, and retirementParticipate as a technical owner in formal governance, release, and incident review processes, with clear escalation paths and responsibilitiesTranslate clinical imaging use cases into deployable AI solutions with defined evaluation metrics, operating thresholds, and reproducible implementation strategiesDesign and execute post-deployment monitoring, including detection and mitigation of model degradation due to distribution shift, scanner changes, or labeling variabilityCollaborate with ML platform, data science, IT, and clinical operations teams to deploy and operate models in secure enterprise environmentsMaintain responsible AI practices, ensuring traceability of data, models, experiments, and documentation of limitations and failure modesContribute to fallback, rollback, and model decommissioning strategies to support patient safety and operational continuityEngage clinical, technical, and operational partners to support safe adoption and communicate model risks, behaviors, and performanceMentor junior team members and contribute to best practices, review standards, and reproducible ML workflowsSkillsBachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering disciplineFive years of experience in machine learning engineering, data science, data engineering, and/or software engineeringExperience developing, deploying, and operating medical imaging ML models in regulated clinical environmentsAbility to build imaging data pipelines involving DICOM workflows, dataset versioning, and distributed trainingDeep proficiency in Python and PyTorch for model training and inference under GPU and memory constraintsExperience orchestrating ML workflows using Airflow, Prefect, or similar DAG-based systemsSkilled in deploying containerized ML workloads on enterprise cloud platforms such as Azure using KubernetesUnderstanding of audit-ready model tracking, lineage, and controlled promotion workflowsAbility to scope medical imaging ML projects end to end, considering clinical and regulatory constraintsExperience designing validation strategies aligned with governance, regulatory expectations, and change control processesKnowledge of healthcare data privacy requirements as they relate to medical imaging and clinical metadataAbility to evaluate model performance quantitatively in the context of clinical workflows and operational realitiesExperience engaging clinicians, patient safety, and business stakeholders to communicate model performance, impacts, and risk considerationsAbility to assess model generalizability and failure modes across scanners, sites, and populationsCollaborate effectively with data scientists, ML engineers, software teams, clinicians, and operational leaders to integrate imaging models into real workflowsProduce clear, comprehensive technical documentation including design specs, validation reports, and runbooksCommunicate project risks, timelines, and outcomes to leadership and governance bodiesContribute to internal technical standards, best practices, and shared ML development frameworksPresent technical and non‑technical updates clearly across multiple stakeholder groupsMaster's Degree or PHD with a concentration in Science, Engineering, or related fieldWith Master's degree, three years' experience requiredWith PhD, one year of experience requiredExperience operating medical imaging ML systems across multiple sites, scanners, or protocols, rather than a single controlled environmentExperience handling post-deployment failures, including performance degradation, clinical incidents, model updates, or corrective actionsExperience raising the technical bar for team members, such as establishing reproducibility practices, review standards, or shared patternsExperience technically evaluating third-party medical imaging AI within clinical workflowsBenefitsPaid medical benefitsGenerous PTOStrong retirement plansTuition benefitsEducational opportunitiesIndividual and team recognitionReferral Bonus Available?Relocation Assistance Available?Company OverviewThe University of Texas MD Anderson Cancer Center is one of the world’s most respected centers devoted exclusively to cancer patient care, research, education and prevention. It was founded in 1994, and is headquartered in Houston, Texas, USA, with a workforce of 10001+ employees. Its website is https://www.mdanderson.org/.Company H1B SponsorshipUT MD Anderson has a track record of offering H1B sponsorships, with 45 in 2026, 291 in 2025, 209 in 2024, 182 in 2023, 168 in 2022, 124 in 2021, 73 in 2020. Please note that this does not guarantee sponsorship for this specific role.