Machine Learning in Biotech Extern

BridgeBio

Mentors

Travis Ahn-Horst, PhD, Computational Biologist

Jin Ju, PhD, Senior Director, Data Science & Operations

About Us

When was the last time you achieved the impossible? If that thought feels overwhelming, you might want to pause here, but if it sparks excitement…read on

In 2015, we pioneered a “moneyball for biotech” approach, pooling projects and promising early-stage research from academia together under one financial umbrella to reduce risk and unleash innovation. This model allows science and small teams of experts to lead the way. We build bridges to groundbreaking advancements in rare disease, and develop life-changing medicines for patients with unmet needs as fast as humanly possible. 

Together we define white space, push boundaries, and empower people to solve problems. If you’re someone who defies convention, join us and work alongside some of the most respected minds in the industry. Together, we’ll ask “why not?” and help reengineer the future of biopharma. At BridgeBio, we value curiosity and experimentation—including the ethical & thoughtful use of AI to improve clarity, speed, and quality of work.

Externship Description

Are you interested in applying the latest machine learning advances to help advance therapeutics for patients with genetic diseases?  This externship focuses on developing computational approaches to represent patient histories and extract insights from medical records with a focus on rare diseases.  The extern will contribute to building a machine learning framework to support rare disease identification and uncover clinically meaningful patterns in patient trajectories.  This project will aim to improve integration across multiple data sources and leverage the rich, longitudinal context within medical records to inform decision-making throughout the drug development process.  This externship is ideal for students interested in machine learning, biomedical data science, and translational applications in drug development.

Specific Objectives

• Develop a machine learning model from real-world clinical data in a rare disease context
• Integrate heterogeneous and longitudinal clinical data (eg. diagnoses, medications, lab values) into a unified representation
• Apply trained models to identify individuals with genetic diseases and characterize disease trajectories
• Present findings and strategic recommendations to the team

Qualifications

• Strong python skills with experience building machine learning models and using version control software
• Familiarity with genomics, biobank, or electronic health record data
• Background in statistics
• Experience with cloud computing environments
• Ability to work independently and communicate results clearly
• Current PhD student or postdoctoral fellow