Summer-Fall 2026 Co-op, Machine Learning for Quantitative Pharmacology, Large Molecules – Machine Learning for Quantitative Pharmacology

Job Description

Machine Learning for Quantitative Pharmacology – Large Molecules Summer-Fall 2026 Co-op | Sanofi

The Tone:
This is a co-op at Sanofi, located in Cambridge, MA. Sanofi is an R&D-driven, AI-powered biopharma company committed to improving people’s lives and delivering compelling growth. They leverage a deep understanding of the immune system and an innovative pipeline to invent medicines and vaccines that treat and protect millions globally. This role drives breakthroughs in drug discovery and development by applying AI/ML models to enhance decision-making, helping turn scientific possibilities into reality for millions.

The TL;DR
• Role: Internship
• Type: Full-time
• Location: In-person, Cambridge, MA

• Team: Quantitative Pharmacology (QP) group
• Mission: Predict pharmacology dynamics of large molecules (biologics) using AI/ML models to aid early drug development decisions.
• Tech Stack: Python, Statistical/Machine Learning

What You’ll Actually Do
• Implement: Implement, develop, and validate data-based AI/ML models.
• Predict: Predict pharmacology dynamics of large molecules by integrating structural, preclinical, and clinical data.
• Aid: Aid early drug development decisions and drive impact across R&D organizations.
• Analyze: Analyze and interpret preclinical and clinical data.
• Design: Design and train Biology- and Pharmacology-based AI/ML models.

The Must-Haves
• Background: Currently enrolled in a PhD program in a STEM field (e.g., Engineering, Computer Science, Mathematics or related field) and enrolled throughout the co-op/internship.
• Experience: Experience with Python and relevant libraries; experience with Statistical/Machine Learning.
• Skills: Data analysis and interpretation; ability to design and train Biology- and Pharmacology-based AI/ML models.
• Bonus: Experience with Large Language models, deep learning, or time series data modeling; familiarity with basic concepts of drug discovery and development, with a focus on biologics; good written, presentation, and verbal communication skills; ability to work in a matrix and global environment.