Technical Lead

Role overview

Responsibilities

• Lead AI solution strategy, architecture, implementation, and lifecycle management for research and biomedical informatics applications.
• Advise NCI leadership and partner with Government stakeholders, researchers, clinicians, and multidisciplinary subject matter experts.
• Oversee AI applications in imaging, radiomics, genomics, multi-omics, oncology, clinical trials, semantic search, knowledge management, and decision support.
• Guide cloud and HPC architecture, technical reviews, roadmaps, executive briefings, and responsible AI practices.
• Mentor technical teams, evaluate emerging technologies, and recommend approaches that advance NCI’s research mission.

Required qualifications

• Master’s degree in a listed computing, AI, biomedical, data science, mathematics, or related discipline.
• At least 15 years of technical leadership on large Federal health, biomedical research, or scientific computing programs.
• At least 10 years leading enterprise AI/ML solutions in healthcare, biomedical research, life sciences, or federal research.
• Experience supporting NIH, NCI, or other Federal biomedical research organizations and advising Government executives.
• Expertise in AI/ML, deep learning, generative AI, NLP, LLMs, computer vision, predictive analytics, biomedical informatics, and cloud-native AI.
• Experience leading multidisciplinary teams and working with HPC, Kubernetes, DevSecOps, and MLOps.
• Ability to obtain and maintain Public Trust / ADPII clearance.

Tools and practices

• Relevant platforms and tools include AWS, Azure, Google Cloud Platform, Kubernetes, TensorFlow, PyTorch, Scikit-learn, Hugging Face, MLflow, Kubeflow, and LangChain.
• Responsible AI expectations include governance, explainability, bias mitigation, privacy, security, and NIST AI Risk Management Framework alignment.

Preferred qualifications

• A Ph.D. in AI, Biomedical Informatics, Computer Science, Computational Biology, or a related field is preferred.
• Experience with NCI organizations, cancer informatics, imaging, radiomics, genomics, precision oncology, clinical trials, or translational research is preferred.
• FHIR, OMOP, FAIR data principles, biomedical ontologies, semantic interoperability, and research data ecosystem expertise are preferred.

Compensation