Machine Learning Engineer - Platforms

As a Machine Learning Engineer - Platforms within the Data Impact & Governance organization, you will shape and scale the enterprise AI/ML platform that powers clinical, research, and operational machine learning across the institution. This is a hands-on engineering role with direct influence on how data science workflows operate institution-wide-enabling safe, efficient, and high-impact AI delivery.

You’ll work with modern cloud and container technologies, MLOps frameworks, and enterprise-grade tools while building solutions that improve patient care, strengthen operations, and accelerate scientific discovery., * Exceptional Benefits: Enjoy paid medical benefits, generous paid time off (PTO), strong retirement plans, and a comprehensive benefits package designed to support your total well-being.

• High-Impact Work: Develop and maintain the platforms that allow clinicians, researchers, and data scientists to bring AI solutions into real-world healthcare environments.
• Cutting-Edge Technology: Work with Dataiku, Kubernetes, Azure, container technologies, and MLOps frameworks that support large-scale enterprise ML operations.
• Career Growth: Collaborate with ML engineers, data scientists, architects, and IT teams, gaining exposure to complex, enterprise-wide AI initiatives and governance.
• Mission-Driven Culture: Your work will contribute directly to improving patient outcomes and advancing research at a nationally recognized cancer center., The Machine Learning Engineer - Platforms supports the development, reliability, and scalability of the enterprise AI/ML platform used across clinical and business operations. The role focuses on MLOps engineering, platform integration, automation, container management, model monitoring, and lifecycle governance. The engineer partners closely with data scientists, ML engineers, and enterprise IT teams to support AI development and deployment, while ensuring compliance, performance, and responsible AI practices.

Major Work Activities

Technical Expertise

• Support development, administration, and maintenance of the enterprise AI/ML platform (Dataiku, Kubernetes, Azure), ensuring scalability, reliability, and smooth integration with institutional systems.
• Orchestrate training, deployment, and inference pipelines within Dataiku targeting Azure and on-premises Kubernetes clusters.
• Develop and maintain MLOps workflows for reproducibility, version control, governance, and model lifecycle management.
• Manage and optimize containerized environments using Docker and Kubernetes to support data science workloads.
• Provide platform support for data scientists and ML engineers, troubleshooting environment, pipeline, and dependency issues.
• Monitor platform performance, cost, security, and compliance, ensuring alignment with enterprise and regulatory standards.

Analytical Skills

• Build and support scalable pipelines in Dataiku, Kubernetes, and Azure, including feature engineering, model tracking, and validation workflows.
• Debug, test, and resolve complex platform or pipeline issues using strong analytical and problem-solving skills.
• Assist with healthcare data integration using standards such as HL7, FHIR, or DICOM when required for model development.

Professionalism: Oral & Written Communication

• Share platform knowledge, best practices, and methodologies through training, documentation, and cross-team collaboration.
• Support analytics and automation workflows by enabling access to data, reviewing project requests, and assisting with interpretation.
• Communicate platform updates, risks, performance, and issue resolutions clearly during meetings and collaborative sessions.
• Work effectively with leaders, technical peers, and end users, ensuring strong communication across both technical and non-technical stakeholders.

Other Duties

• Perform additional tasks as assigned to support the AI/ML platform, MLOps practices, and enterprise data science initiatives., This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.

• Required: Bachelor’s Degree Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline.
• Preferred: Master’s Degree Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline.

WORK EXPERIENCE

• Required: 3 years in machine learning engineering, data science, data engineering, and/or software engineering experience.
• Required: 1 year experience with Master’s degree.
• No experience required with PhD.

Preferred Experience/Skills: Healthcare experience needed, experience with MLOps platforms and/or cloud AI certifications, strong proficiency in CI/CD and automation of the AI lifecycle, experience working on healthcare focused machine learning projects. Experience with Azure and/or Kubernetes. Proficiency in services such as Azure Kubernetes Services and Azure ML (or similar).

The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition.