AI​/ML Engineer Precision Oncology

AI/ML Engineer
- Precision Oncology

The AI/ML Engineer
- Precision Oncology develops, deploys, and scales advanced artificial intelligence technologies for precision oncology. This role builds and manages the software platforms, data pipelines, computational infrastructure, and machine learning systems required to support AI-enabled cancer research and clinical outcome optimization initiatives. The AI/ML Engineer works at the intersection of machine learning engineering, multimodal data integration, foundation models, generative AI, biomedical data science, and cancer research.

This position collaborates closely with AI Data Scientists, clinicians, and informaticians to translate innovative AI methodologies into robust, reproducible, and institutionally scalable solutions for oncology applications.

Join Moffitt Cancer Center's Precision Oncology and AI initiatives as an AI/ML Engineer and help transform groundbreaking research into scalable, real-world solutions. This role sits at the intersection of machine learning engineering, multimodal data integration, generative AI, biomedical data science, and oncology, building the platforms, pipelines, and AI systems that accelerate cancer research and improve patient outcomes.

Our mission is to improve cancer patient outcomes through AI-enabled precision oncology and clinical decision support. We develop and deploy multimodal AI approaches that integrate clinical, imaging, pathology, molecular, genomic, and longitudinal outcomes data to generate actionable insights that support personalized cancer care.

Working at the intersection of clinical care, translational research, and artificial intelligence, our team collaborates directly with oncologists, radiologists, pathologists, surgeons, nurses, and multidisciplinary care teams to develop solutions that address real-world challenges in cancer diagnosis, treatment planning, response assessment, and survivorship. By translating advances in machine learning, real-world evidence, and multimodal AI into clinically meaningful applications, we strive to deliver the right information to the right clinician at the right time, helping improve outcomes and advance the future of precision oncology.

Position Highlights

• Design and deploy enterprise-scale AI and machine learning systems that support cancer research and AI-enabled clinical outcome optimization initiatives.
• Work with cutting-edge technologies including foundation models, large language models (LLMs), multimodal AI systems, and generative AI applications.
• Develop scalable AI platforms integrating clinical, imaging, pathology, molecular, genomic, and outcomes data.
• Collaborate with AI Data Scientists, clinicians, and informaticians to deliver innovative solutions with real-world impact across oncology research and healthcare.

The Ideal Candidate

• Passionate about building robust AI systems that advance scientific discovery and improve patient outcomes.
• Brings a strong understanding of real-world oncology data, with experience working across clinical and translational cancer datasets and an appreciation for the challenges of developing AI solutions that support clinical decision-making and improve patient outcomes.
• Experienced in machine learning engineering, software development, cloud computing, and scalable data infrastructure.
• Thrives in a collaborative environment and enjoys transforming complex research concepts into production-ready applications.
• Keeps pace with emerging AI technologies and is excited to help shape the future of precision oncology.

Responsibilities

• Design, develop, deploy, and maintain scalable AI and machine learning systems for precision oncology applications.
• Build and manage AI platforms that integrate multimodal clinical and research datasets.
• Develop scalable data pipelines, model-training workflows, inference services, and software infrastructure supporting AI initiatives.
• Implement machine learning operations (MLOps) best practices, including model versioning, experiment tracking, deployment, monitoring, and governance.
• Develop and optimize foundation models, generative AI solutions, large language models (LLMs), vision-language models (VLMs), and other AI applications.
• Create APIs, software services, and user-facing applications that integrate AI capabilities into research, clinical, and operational environments.
• Evaluate and implement emerging AI technologies, engineering frameworks, and best practices that support institution-wide AI innovation.
• Collaborate closely with scientists, clinicians, and technical teams to translate novel AI methodologies into scalable solutions.

Credentials and Qualifications

Required

• Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Data Science, Artificial Intelligence, Biomedical Engineering, Informatics, or a related quantitative discipline. Bachelor's degree may be considered with additional relevant experience.
• Three (3) years of professional experience developing and deploying machine learning…