2026 AI/ML Biotherapeutics Development Intern (PhD)

Company Description

AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.

Job Description

AI/ML Biotherapeutics Development Internship Overview 

Envision spending your summer working with energetic colleagues and inspirational leaders, all while gaining world-class experience in one of the most dynamic organizations in the pharmaceutical industry. This is a reality for AbbVie Interns.  

The Quantitative, Translation & ADME Sciences (QTAS) team in South San Francisco supports the characterization, modeling the ADME (Absorption, Distribution, Metabolism, and Excretion) aspects of drug development research, part of which focuses on enhancing biologics property prediction through innovative AI/ML methods. This internship involves exploring explicit structural data integration into deep learning models for biologics sequence-to-property prediction, tapping into dynamic protein conformations through ensemble structures.  

Key responsibilities include: 

• Evaluate and analyze structures of biologics. 

• Explore and implement state-of-the-art techniques to improve AI/ML models. 

• Collaborate with AI/ML and computational biology experts to refine modeling strategies and achieve predefined success metrics 

Qualifications

Minimum Qualifications  

• Currently enrolled in university, pursuing a PhD in Computer Science, Computational Biology, Biophysics, or other related field. 

• Must be enrolled in university for at least one semester following the internship.  

• Demonstrated experience with deep learning concepts and experience with Python and PyTorch. 

• Familiarity with protein language models. 

• Familiarity with protein struct