Machine learning applied to Quantitative Pharmacology - Small molecules Fall 2026 Co-op

Sanofi•Cambridge, MA

12d•Onsite

About The Position

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 (ML) co-op to be part of the implementation and development of ML models to enhance decision making across drug discovery and development. The scope of responsibility will involve aiding the group in the development of machine ML models to support drug prioritization and contributing to end‑to-end model development using structural characteristics to predict pharmacokinetic and pharmacology dynamics of small molecules relevant for early drug development decisions. In support of these activities, the successful incumbent should be able to analyze and interpret preclinical and clinical data and be part of the QP team that develops ML approaches to support critical decision making in drug research. The QP group supports multiple therapeutic areas and research platforms within the broader R&D organization.

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 40 hrs./week, Monday-Friday, for the full duration of the internship/co-op
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 deep learning framework and relevant libraries such as RDkit, PyTorch, TensorFlow, Keras, Scikit-learn, Pandas etc.

Nice To Haves

Familiarity with Time series modelling, Natural Language Processing, Neural Networks and Deep learning framework.
Familiarity in developing dynamical (mathematical) and statistical/machine learning models.
Ability to work in a matrix and in a global environment.
Good written, presentation and verbal communication skills are essential.

Responsibilities

aiding the group in the development of machine ML models to support drug prioritization
contributing to end‑to-end model development using structural characteristics to predict pharmacokinetic and pharmacology dynamics of small molecules relevant for early drug development decisions
analyze and interpret preclinical and clinical data
be part of the QP team that develops ML approaches to support critical decision making in drug research