Machine Learning Quantitative Pharmacology – Molecules Summer-Fall Co-op

Job Title

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

Cambridge, MA

Join the Quantitative Pharmacology group at Sanofi to develop AI/ML models that predict pharmacology dynamics of large molecules (biologics). You will help drive decision making across early drug development stages and support various R&D organizations.

• Implement, develop, and validate data‑based AI/ML models to enhance decision making.
• Analyze and interpret preclinical and clinical data.
• Design and train biology- and pharmacology-based AI/ML models.
• Support critical decision making as part of the QP team across multiple therapeutic areas.

• Currently enrolled in a PhD program in a STEM field (Engineering, Computer Science, Mathematics or related field).
• Enrolled in an accredited college or university throughout the duration of the co‑op/internship.
• Able to relocate to the office location and work 40hrs/week, Monday‑Friday, for the full duration of the co‑op/internship.
• 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.
• Experience with Python and relevant libraries.
• Experience with statistical/machine learning.
• Experience with large language models, deep learning, or time‑series data modeling preferred.

• Familiarity with basic concepts of drug discovery and development, especially biologics.
• Good written, presentation, and verbal communication skills.
• Ability to work in a matrix and global environment.

• Supportive, future‑focused team.
• Opportunities for growth and career development.
• Exposure to cutting‑edge technologies and research methodologies.
• Networking opportunities within Sanofi and the broader biotech community.
• Well‑crafted rewards package.

Sanofi Inc. and its U.S. affiliates are Equal Opportunity and Aff… 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.