Senior Machine Learning Engineer

Senior Machine Learning Engineer - Healthcare Senior Machine Learning Engineer - Healthcare at the University of Texas MD Anderson Cancer Center, part of the Komen Graduate Training Program. Summary The mission of The University of Texas M. D. Anderson Cancer Center is to eliminate cancer worldwide through patient care, research, prevention, and education. We are building a dynamic team of machine learning engineers and data scientists to accelerate responsible AI across the enterprise, driving lasting improvements in cancer care. Key Responsibilities Oversee the full AI model lifecycle: training, evaluation, deployment, monitoring, and maintenance of production‑quality models. Develop and maintain CI/CD pipelines for model training, deployment, and monitoring, ensuring security, scalability, reliability, reproducibility, and performance. Provide rigorous testing, versioning, and documentation to mitigate impact and risk while ensuring reproducibility. Promote responsible AI practices by minimizing bias, enhancing fairness, and maximizing transparency. Maintain meticulous records of experiments, data and model lineage tracking, and model scorecards. Engage stakeholders to gather requirements, communicate AI concepts, and capture feedback. Design fallback and decommissioning strategies to ensure operational continuity. Support the evaluation and onboarding of third‑party models, ensuring they meet institutional standards and minimize organizational risk. Deliver training on AI solutions to improve organization‑wide understanding and application. Stay abreast of technology trends, contribute to tech communities, and foster 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. Experienced 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. Knowledgeable about AI/ML platform infrastructure, including cloud and on‑premises architectures. Familiar with cloud‑native tools, services, and computing environments (Azure, AWS, GCP). Proficient in DevOps practices and CI/CD pipelines, including Azure DevOps and GitHub Actions. Experienced with containerization using Docker and orchestration with Kubernetes, along with DAG tools. Analytical Expertise Skilled in project management methodologies (SAFe Agile, PRINCE2, Lean) for end‑to‑end AI/ML lifecycle management. In-depth knowledge of AI/ML model lifecycle management aligned with ISO standards. Proficient in decision‑making, problem‑solving, and executing AI/ML healthcare solutions. Skilled at quantitatively assessing machine learning models for performance, workflow impact, and potential risks. Adept at collaborating with vendors and partners for evaluating and integrating third‑party AI solutions. 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 models into existing systems. Document CI/CD pipelines, deployment workflows, and infrastructure set‑ups. Report project metrics, including progress, impact, and risks, to leadership with strategic recommendations. Manage stakeholder relations to facilitate solution adoption and address issues. Share knowledge and offer technical assistance to researchers and colleagues. Deliver both technical and non‑technical updates in meetings and professional gatherings. Engage effectively with team leaders, peers, end‑users, and support staff as needed. Other duties as assigned. Education Required ‑ Bachelor’s degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or a related engineering discipline. Preferred Education ‑ Master’s level degree. Experience Required Five years of experience in machine learning engineering, data science, data engineering, and/or software engineering. With a Master’s degree, three years’ experience; with a PhD, one year’s experience. Preferred Experience Experience developing MLOps pipelines for computer vision AI models. Hands‑on experience developing custom machine learning algorithms from scratch. Designed and implemented shared machine learning services used across multiple teams or products. Led the development of systems that automate the deployment and maintenance of multiple machine learning models into user‑facing products. At least five years of industry experience in data science, with at least three years as a Senior Machine Learning Engineer. Benefits and Salary Minimum Salary: US$146,500 Midpoint Salary: US$183,000 Maximum Salary: US$219,500 Work Location: Remote (within Texas only) Employment Status: Full‑Time Employee Status: Regular Work Week: Days Fund Type: Hard FLSA: Exempt and not eligible for overtime pay Referral Bonus Available: Yes Relocation Assistance Available: Yes Science Jobs: No Requisition ID: 176014 Pivotal Position: Yes Equal Employment Opportunity The University of Texas MD Anderson Cancer Center provides equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state, or local laws unless such distinction is required by law. Legal Statement #J-18808-Ljbffr