Senior Machine Learning Engineer

Senior Machine Learning Engineer - Healthcare Join to apply for the Senior Machine Learning Engineer - Healthcare role at Komen Graduate Training Program UT MDACC. Summary The mission of The University of Texas M. D. Anderson Cancer Center is to eliminate cancer in Texas, the nation, and the world through outstanding programs that integrate patient care, research, prevention, and education. Core to the success of our mission is the ability to orchestrate 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 that can help us consistently and responsibly accelerate the impact of AI across the enterprise, driving long‑lasting improvements in cancer care. Key Responsibilities Oversee the lifecycle of AI models (training, evaluation, deployment, monitoring, maintenance) in compliance with standards and best practices. Develop CI/CD pipelines for ML model training, deployment, and monitoring while upholding security, scalability, reliability, reproducibility, and performance. Provide rigorous testing, versioning, and documentation, ensuring impact, risk mitigation, and reproducibility. Develop and support a responsible AI culture by minimizing bias, enhancing fairness, and maximizing transparency in AI models. Maintain diligent records of model development experiments, data and model lineage tracking, and model scorecards. Engage with stakeholders to gather requirements, convey AI concepts understandably, and capture feedback. Design fallback and decommissioning strategies to ensure operational continuity. Support the evaluation and onboarding of third‑party machine learning models, ensuring they meet institutional standards. Deliver training on AI solutions to enhance understanding and application across the organization. Engage with 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 constructing 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 project lifecycle. 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. Document CI/CD pipelines, deployment workflows, and infrastructure setups. Report project metrics to leadership, offering strategic recommendations for AI/ML prioritization. Manage stakeholder relations to facilitate solution adoption and address issues. Share knowledge and offer technical assistance to researchers and colleagues. Deliver technical and non‑technical updates in meetings and professional gatherings. Engage effectively with team leaders, peers, end‑users, and support staff. Education and Experience Education Required: Bachelor’s degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or related discipline. Preferred Education: Master’s level degree. Experience Required: Five years in machine learning, data science, data engineering, or software engineering (adjusted for advanced degrees). Preferred Experience: MLOps pipeline development, custom ML algorithm design, system leadership, senior engineering experience. Benefits Medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition. Security and Eligibility 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 may require routine reviews and screening. Compliance with all security requirements is a condition of hire and continued employment. Equal Employment Opportunity Statement It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity or expression, disability, protected veteran status, genetic information, or any other basis protected by institutional or federal, state, or local laws unless such distinction is required by law. Reference: http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html Additional Information Requisition ID: 176014 Employment Status: Full‑Time Employee Status: Regular Work Week: Days Minimum Salary: US Dollar (USD) 146,500 Midpoint Salary: US Dollar (USD) 183,000 Maximum Salary: US Dollar (USD) 219,500 FLSA: exempt and not eligible for overtime pay Fund Type: Hard Work Location: Remote (within Texas only) Pivotal Position: Yes Referral Bonus Available: Yes Relocation Assistance Available: Yes Science Jobs: No #J-18808-Ljbffr