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

Job Title:

Summer-Fall 2026 Co-op, Machine Learning for

Quantitative Pharmacology, Large Molecules Location:

Cambridge, MA About the Job Join the engine of Sanofi's mission — where deep immunoscience meets bold, AI-powered research. In R D, you'll drive breakthroughs that could turn the impossible into possible for millions. The Quantitative Pharmacology (QP) group in Sanofi is seeking a Machine Learning co-op to implement, develop and validate data-based (AI/ML) models to enhance decision making across drug discovery and development. The focus will be on predicting pharmacology dynamics of large molecules (biologics) by integrating structural properties, preclinical and clinical data. The resulting AI/ML models will aid in early drug development decisions and drive impact across multiple R D organizations. In support of these activities, the successful incumbent should be able to analyse and interpret preclinical and clinical data, design and train Biology- and Pharmacology-based AI/ML models and support critical decision making as part of the QP team. The QP group supports multiple therapeutic areas and research platforms within the broader R D organization.

About Sanofi:

We're an R D-driven, AI-powered biopharma company committed to improving people's lives and delivering compelling growth. Our deep understanding of the immune system - and innovative pipeline - enables us to invent medicines and vaccines that treat and protect millions of people around the world. Together, we chase the miracles of science to improve people's lives.

About You Basic Requirements:

Currently enrolled in a PhD program in a STEM field (e.g. Engineering, Computer Science, Mathematics or related field) Must be enrolled in an accredited college or university throughout the duration of the co-op/internship Must be able to relocate to the office location and work 40hrs/week, Monday-Friday, for the full duration of the co-op/internship Must be permanently authorized to work in the U.S. and not require sponsorship of an employment visa (e.g., H-1B or green card) at the time of application or in the future. Students currently on CPT, OPT, or

STEM OPT

usually require future sponsorship for long term employment and do not meet the requirements for this program unless eligible for an alternative long-term status that does not require company sponsorship Experience with Python and relevant libraries Experience with Statistical/Machine Learning Experience with Large Language models/ deep learning/ time series data modeling preferred

Preferred Qualifications:

Familiarity with basic concepts of drug discovery and development. Focus on biologics preferred. Good written, presentation and verbal communication skills are essential. Ability to work in a matrix and in a global environment.

Why Choose Us:

Bring the miracles of science to life alongside a supportive, future-focused team. Discover endless opportunities to grow your talent and drive your career, whether it's through a promotion or lateral move, at home or internationally. Enjoy a thoughtful, well-crafted rewards package that recognizes your contribution and amplifies your impact. Exposure to cutting -edge technologies and research methodologies. Networking opportunities within Sanofi and the broader biotech community. Sanofi Inc. and its U.S. affiliates are Equal Opportunity and Affirmative Action employers committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status; domestic violence victim status; atypical cellular or blood trait; genetic information (including the refusal to submit to genetic testing) or any other characteristic protected by law. #GD-SA #LI-SA #LI-Onsite #vhd null