AI/ML Engineer Precision Oncology

Summary

Moffitt Cancer Center is a National Cancer Institute-designated Comprehensive Cancer Center dedicated to cancer prevention, treatment, and research. The AI/ML Engineer - Precision Oncology develops, deploys, and scales AI technologies, software platforms, data pipelines, computational infrastructure, and machine learning systems for precision oncology, cancer research, and clinical outcome optimization. The role collaborates with scientists, clinicians, and informaticians to translate AI methodologies into scalable clinical and research solutions.

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

Skills

• * 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 systems, data platforms, or AI-enabled software solutions
• Relevant experience may be gained through master's degree research
• * Strong software engineering and programming skills in Python
• * Experience developing and deploying machine learning and deep learning applications using PyTorch, Hugging Face, or similar frameworks
• * Experience building large-scale data processing pipelines and supporting AI systems in cloud, HPC, GPU, or distributed computing environments
• * Experience with MLOps, Git, containerization technologies, and production-grade AI software development
• * Experience with foundation models, LLMs, vision-language models (VLMs), multimodal AI systems, or generative AI applications
• * Experience integrating clinical, imaging, pathology, molecular, genomic, and outcomes data into AI solutions
• * Experience with cloud-native AI platforms and services
• * Experience with CI/CD pipelines, workflow orchestration, infrastructure automation, and AI governance best practices
• * Experience with vector databases, semantic search, retrieval-augmented generation (RAG), retrieval systems, or agentic AI frameworks

Qualifications

Must Haves

• * 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 systems, data platforms, or AI-enabled software solutions
• Relevant experience may be gained through master's degree research
• * Strong software engineering and programming skills in Python
• * Experience developing and deploying machine learning and deep learning applications using PyTorch, Hugging Face, or similar frameworks
• * Experience building large-scale data processing pipelines and supporting AI systems in cloud, HPC, GPU, or distributed computing environments
• * Experience with MLOps, Git, containerization technologies, and production-grade AI software development

Nice to Haves

• * Experience with foundation models, LLMs, vision-language models (VLMs), multimodal AI systems, or generative AI applications
• * Experience integrating clinical, imaging, pathology, molecular, genomic, and outcomes data into AI solutions
• * Experience with cloud-native AI platforms and services
• * Experience with CI/CD pipelines, workflow orchestration, infrastructure automation, and AI governance best practices
• * Experience with vector databases, semantic search, retrieval-augmented generation (RAG), retrieval systems, or agentic AI frameworks

Benefits

• Hybrid (Part -time in Office)
• Eligible for an annual Team Member Incentive.
• Eligible for an annual Team Member Merit Increase.
• Offered a comprehensive benefits package including health, financial, and lifestyle coverage.