Technical Lead

Role overview

Responsibilities

• Lead technical strategy, architecture, and implementation of AI-enabled research and biomedical informatics solutions; advise NCI leadership.
• Partner with government and scientific stakeholders to set priorities, evaluate technologies, and deliver mission-focused solutions.
• Guide AI model development, deployment, and lifecycle management across ML, deep learning, NLP, LLMs, computer vision, analytics, and multi-agent systems.
• Oversee solutions for biomedical imaging, radiomics, genomics, multi-omics, precision oncology, clinical trials, semantic search, and decision support.
• Guide cloud and HPC architectures using AWS, Azure, Google Cloud, Kubernetes, and MLOps.
• Ensure responsible AI, governance, explainability, bias mitigation, privacy, security, and policy compliance.
• Lead technical reviews, briefings, and roadmaps; mentor and oversee multidisciplinary teams.
• Evaluate emerging technologies and recommend approaches that advance scientific discovery and NCI's mission.

Required qualifications

• Master's degree in a related discipline.
• 15+ years of technical leadership on large-scale federal health, biomedical research, or scientific computing programs.
• 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.
• Expertise in AI/ML, generative AI, NLP, LLMs, computer vision, predictive analytics, biomedical informatics, and cloud-native AI.
• Experience advising government executives and leading multidisciplinary technical teams.
• Experience with HPC, cloud-native AI platforms, Kubernetes, DevSecOps, and MLOps.
• Ability to obtain and maintain Public Trust / ADPII Clearance.

Preferred qualifications

• Ph.D. in AI, biomedical informatics, computer science, computational biology, or related field is preferred.
• Experience with NCI organizations and cancer informatics, imaging, radiomics, genomics, precision oncology, clinical trials, or translational research is preferred.
• FHIR, OMOP, FAIR data, biomedical ontologies, semantic interoperability, and research data ecosystems are preferred.

Compensation

• Pay range is $131,300–$237,350; the range is a guideline and not a guarantee of compensation.