About Fulgent
Founded in 2011, Fulgent has evolved into a premier, full-service genomic testing company built around a foundational technology platform.
Through our diverse testing menu, Fulgent is focused on transforming patient care in oncology, anatomic pathology, infectious and rare diseases, and reproductive health. We believe that by providing a wide range of effective, flexible testing options in conjunction with best-in-class service and support, we can redefine the way medicine is managed for patients and clinicians alike.
Since integrating with our therapeutic development business, Fulgent is also developing drug candidates for treating a broad range of cancers using a novel nanoencapsulation and targeted therapy platform. By merging our fields of expertise, we aim to become a fully integrated precision medicine company.
Summary
As Director of Clinical Artificial Intelligence, you will lead program development and deploy artificial intelligence (AI) and machine learning (ML) to advance clinical diagnostics. This key role will guide R&D efforts to improve disease detection using digital pathology and improve genomic variant curation/reporting efficiency. You will possess a robust blend of technical expertise in AI/ML and an understanding of its clinical applications in oncology, reproductive medicine, and rare diseases.
**Preference will be given to candidates who are located close to one of our laboratory sites in the following cities: Los Angeles, CA - Phoenix, AZ - Dallas, TX - Atlanta, GA - Boston, MA. Hybrid on-site work is preferred.**
Key Job Elements
Leadership and Strategic Direction
- Provide strategic leadership for the Clinical AI development team, crafting and executing a vision for AI-driven innovations in diagnostics.
- Collaborate with cross-functional teams, including R&D, medical affairs, program management, and software development, to design and deploy AI models that enhance diagnostic and prognostic.
Clinical Large-Language Models
- Spearheaded the optimization and creation of biological models to generate insights into disease mechanisms, predict outcomes, and enhance variant curation and reporting.
- Oversee using LLMs to efficiently extract critical information from electronic health records and scientific publications, enhancing the foundation for diagnostic decisions.
- Treat data with a high level of integrity and ethics.
Genomic & Multi-Omics Data Analysis
- Direct projects that apply ML algorithms for examining tumor genetic data, aiming to identify critical mutations and biomarkers that inform personalized treatment approaches.
- Guide the integration of various "omics" data sources (genomics, proteomics, metabolomics) using AI to achieve a holistic view of cancer biology, opening up new avenues for diagnostic markers and treatment targets.
Digital Pathology Image Analysis
- Lead the development of AI algorithms to enhance digital pathology analysis, focusing on cancer cell detection, tumor grading, and heterogeneity analysis, to support accurate and detailed diagnostics.
