Senior AI Scientist

About Our Client

The organization is a membership-based primary and specialty health care practice focused on prevention and longevity. It integrates a multidisciplinary team of physicians to provide proactive, preventive, and precision-based care for members and their families. Services include primary care, advanced screening and diagnostics, urgent and specialty care, 24/7 home visits, and imaging, all covered by an annual membership fee. The organization operates state-of-the-art facilities in multiple U.S. locations and emphasizes personalized preventive medicine grounded in current scientific research.

About the Opportunity

The Senior AI Scientist will lead the development of medical models central to the clinical AI agenda, focusing on domain-specific models for key clinical challenges and foundational models that predict health trajectories for personalized prevention. This role utilizes extensive, complex datasets including genome sequencing, advanced imaging, longitudinal labs, wearable data, and multi-generational records. The position aims to create models that significantly improve clinical outcomes beyond standard options, contributing directly to advancing preventive medicine.

Responsibilities

• Own the medical modeling roadmap, selecting effective model families and training methods.
• Evaluate and recommend open-source models for fine-tuning or development.
• Design and validate new model architectures, especially for longitudinal multimodal data.
• Lead fine-tuning and post-training techniques including PEFT, DPO, RLHF, and distillation.
• Curate high-quality training datasets with appropriate handling of confidential information.
• Conduct rigorous evaluations including clinical benchmarks and subgroup performance analyses.
• Manage training and evaluation infrastructure with reproducible experimentation.
• Stay current with relevant literature and synthesize applicable advancements.
• Collaborate with clinicians and research partners to define model success and joint projects.

Requirements

• Graduate degree in computer science, machine learning, computational biology, biomedical informatics, or related field; PhD preferred or equivalent open-source experience.
• Minimum four years of hands-on experience training and fine-tuning deep learning models.
• Expertise in modern fine-tuning and post-training methods such as SFT, PEFT, preference tuning, and distillation.
• Strong knowledge of the open-source model ecosystem and state-of-the-art models.
• Proficiency in Python, PyTorch, and frameworks like Hugging Face for model development.
• Experience with distributed training, mixed precision, data loading, and experiment tracking tools.
• Strong evaluation discipline including benchmarking and ablation studies.

Pay Range and Compensation Package

The pay range and compensation package for this role will be determined based on the candidate’s experience, skills, and other relevant factors.

Equal Opportunity Statement

Our client is an equal opportunity employer. They celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, or national origin.