MD Anderson Cancer Center
Senior Machine Learning Engineer - Healthcare
MD Anderson Cancer Center, Houston, Texas, United States, 77246
Overview
The University of Texas M. D. Anderson Cancer Center seeks to eliminate cancer through programs that integrate patient care, research, prevention, and education. The mission emphasizes orchestrating multidimensional data, data analytics, and machine learning to create sustainable impact within a framework of responsible AI. We are building a dynamic team of machine learning engineers and data scientists to accelerate AI’s impact across the enterprise, driving long-lasting improvements in cancer care. Responsibilities
Oversee the lifecycle of AI models, including training, evaluation, deployment, monitoring, and maintenance of production-quality machine learning models, in compliance with standards and best practices. Develop CI/CD pipelines for ML model training, deployment, and monitoring, ensuring security, scalability, reliability, reproducibility, and performance. Provide rigorous testing, versioning, and documentation to ensure impact, risk mitigation, and reproducibility. Develop and support a culture of responsible AI by minimizing bias, enhancing fairness, and maximizing transparency in AI models. Maintain records of model development experiments, data and model lineage tracking, and data/model scorecards. Engage with stakeholders to gather requirements, convey AI concepts understandably, and capture feedback. Design fallback and decommissioning strategies for AI solutions to ensure operational continuity. Support evaluation and onboarding of third-party ML models, ensuring they meet institutional standards and minimize organizational risk. Deliver training on AI solutions to enhance understanding and application across the organization. Engage with technology trends, contribute to tech communities, and foster a culture of continuous learning and innovation. Technical Expertise
Proficient in developing, deploying, and maintaining AI/ML algorithms in production environments. Skilled in building scalable data pipelines, feature and artifact management, and analytics. Experience with MLOps tools and processes for data, code, and model management. Strong proficiency in Python and either C++ or C#, with practical knowledge of TensorFlow, PyTorch, and Scikit-learn. Knowledge of AI/ML platform infrastructure, including cloud and on-premises architectures. Familiar with cloud-native tools, services, and computing environments (e.g., Azure, AWS, GCP). Proficient in DevOps practices and CI/CD pipelines, including Azure DevOps and GitHub Actions. Experience with containerization using Docker and orchestration with Kubernetes, along with DAGs tools. Analytical Expertise
Skilled in project management methodologies (SAFe Agile, PRINCE2, Lean) for end-to-end AI/ML project lifecycle management, ensuring timely delivery, budget adherence, and quality compliance. Knowledge of AI/ML Model Lifecycle Management aligned with ISO standards for software and AI development. Proficient in decision-making, problem-solving, and executing AI/ML healthcare solutions. Ability to quantitatively assess machine learning models for performance, workflow impact, and potential risks. Adept at collaborating with vendors and partners to evaluate and integrate third-party AI solutions into current systems and processes. Competent in identifying risks and formulating mitigation plans to prevent project delays. Oral and Written Communication
Collaborate with data scientists, ML engineers, and software engineers to integrate ML models into existing systems. Document CI/CD pipelines, deployment workflows, and infrastructure setups. Report project metrics, including progress, impact, and risks, to leadership, offering strategic recommendations for AI/ML use-case prioritization. Manage stakeholder relations to facilitate solution adoption and address issues. Share knowledge and provide technical assistance to researchers and colleagues. Deliver technical and non-technical updates in meetings and at professional gatherings. Engage effectively with team leaders, peers, end-users, and support staff as needed. Education and Experience
Education: Bachelor’s degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or related engineering discipline. Master’s degree preferred. Experience: Five years of experience in machine learning engineering, data science, data engineering, and/or software engineering. With a Master’s degree, three years; with a PhD, one year. Preferred experience includes developing MLOps pipelines for computer vision AI models and hands-on development of custom machine learning algorithms from scratch; leading the deployment and maintenance of ML models in user-facing products; and five years of industry experience in data science (with at least 3 years as a Senior Machine Learning Engineer). The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and recognition. This position may require routine security and integrity reviews of critical infrastructure as defined by Texas law. Employment is contingent upon meeting security requirements. MD Anderson is an equal employment opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by law.
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The University of Texas M. D. Anderson Cancer Center seeks to eliminate cancer through programs that integrate patient care, research, prevention, and education. The mission emphasizes orchestrating multidimensional data, data analytics, and machine learning to create sustainable impact within a framework of responsible AI. We are building a dynamic team of machine learning engineers and data scientists to accelerate AI’s impact across the enterprise, driving long-lasting improvements in cancer care. Responsibilities
Oversee the lifecycle of AI models, including training, evaluation, deployment, monitoring, and maintenance of production-quality machine learning models, in compliance with standards and best practices. Develop CI/CD pipelines for ML model training, deployment, and monitoring, ensuring security, scalability, reliability, reproducibility, and performance. Provide rigorous testing, versioning, and documentation to ensure impact, risk mitigation, and reproducibility. Develop and support a culture of responsible AI by minimizing bias, enhancing fairness, and maximizing transparency in AI models. Maintain records of model development experiments, data and model lineage tracking, and data/model scorecards. Engage with stakeholders to gather requirements, convey AI concepts understandably, and capture feedback. Design fallback and decommissioning strategies for AI solutions to ensure operational continuity. Support evaluation and onboarding of third-party ML models, ensuring they meet institutional standards and minimize organizational risk. Deliver training on AI solutions to enhance understanding and application across the organization. Engage with technology trends, contribute to tech communities, and foster a culture of continuous learning and innovation. Technical Expertise
Proficient in developing, deploying, and maintaining AI/ML algorithms in production environments. Skilled in building scalable data pipelines, feature and artifact management, and analytics. Experience with MLOps tools and processes for data, code, and model management. Strong proficiency in Python and either C++ or C#, with practical knowledge of TensorFlow, PyTorch, and Scikit-learn. Knowledge of AI/ML platform infrastructure, including cloud and on-premises architectures. Familiar with cloud-native tools, services, and computing environments (e.g., Azure, AWS, GCP). Proficient in DevOps practices and CI/CD pipelines, including Azure DevOps and GitHub Actions. Experience with containerization using Docker and orchestration with Kubernetes, along with DAGs tools. Analytical Expertise
Skilled in project management methodologies (SAFe Agile, PRINCE2, Lean) for end-to-end AI/ML project lifecycle management, ensuring timely delivery, budget adherence, and quality compliance. Knowledge of AI/ML Model Lifecycle Management aligned with ISO standards for software and AI development. Proficient in decision-making, problem-solving, and executing AI/ML healthcare solutions. Ability to quantitatively assess machine learning models for performance, workflow impact, and potential risks. Adept at collaborating with vendors and partners to evaluate and integrate third-party AI solutions into current systems and processes. Competent in identifying risks and formulating mitigation plans to prevent project delays. Oral and Written Communication
Collaborate with data scientists, ML engineers, and software engineers to integrate ML models into existing systems. Document CI/CD pipelines, deployment workflows, and infrastructure setups. Report project metrics, including progress, impact, and risks, to leadership, offering strategic recommendations for AI/ML use-case prioritization. Manage stakeholder relations to facilitate solution adoption and address issues. Share knowledge and provide technical assistance to researchers and colleagues. Deliver technical and non-technical updates in meetings and at professional gatherings. Engage effectively with team leaders, peers, end-users, and support staff as needed. Education and Experience
Education: Bachelor’s degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or related engineering discipline. Master’s degree preferred. Experience: Five years of experience in machine learning engineering, data science, data engineering, and/or software engineering. With a Master’s degree, three years; with a PhD, one year. Preferred experience includes developing MLOps pipelines for computer vision AI models and hands-on development of custom machine learning algorithms from scratch; leading the deployment and maintenance of ML models in user-facing products; and five years of industry experience in data science (with at least 3 years as a Senior Machine Learning Engineer). The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and recognition. This position may require routine security and integrity reviews of critical infrastructure as defined by Texas law. Employment is contingent upon meeting security requirements. MD Anderson is an equal employment opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by law.
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