AI​/ML Engineer, Precision Oncology

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

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

Demonstrates expertise in designing and deploying scalable AI and machine learning systems, with a strong focus on MLOps best practices and integration of multimodal datasets. Proficient in developing data pipelines and AI applications that enhance precision oncology and clinical research.

• Machine Learning System Development
• MLOps Implementation
• Python Programming
• Data Pipeline Engineering
• AI Application Development

• Machine Learning
• Deep Learning
• Data Processing Pipelines
• AI Software Development
• Model Training Workflows
• Generative AI Solutions
• Large Language Models
• Vision-Language Models
• APIs Development
• Experiment Tracking

• Collaboration
• Communication

• Precision Oncology
• Multimodal AI Systems
• AI Governance
• Emerging AI Technologies Clinical Data Integration

• Py Torch
• Hugging Face
• Git
• Containerization Technologies
• Cloud Computing
• HPC
• GPU
• CI/CD Pipelines
• Vector Databases
• Workflow Orchestration