Grad Intern - Computational Chemistry (summer 2027)

What You Will Do

We are looking for a summer intern to join the Computational Chemistry team in the summer of 2027. Computational chemistry plays a crucial role in our drug discovery engine, speeding up our pipeline from target inception to drug development. During this program, you will develop and evaluate molecular property prediction models on predictive performance and interpretability that will help guide the successful discovery of small molecule therapeutics.

• Develop and evaluate machine learning models for molecular property prediction, assess predictive performance using appropriate metrics, and explore approaches to making these models accessible through conversational AI agents
• Apply model interpretability methods to investigate how molecular features influence predictions and explore structure–property relationships
• Work collaboratively with interdisciplinary teams to ensure the successful execution and implementation of the project
• Review and summarize project-related literature
• Communicate findings and progress effectively to team members
• Clearly document methods and analyses to allow reproducible research

Program Eligibility Requirements

• Must be available to work a full-time schedule
• Expected continued enrollment in an accredited college or university following the internship program / co-op
• Student must be located in the United States for the duration of the internship program

Basic Qualifications

• 18 years or older
• Graduated with a bachelor's degree from an accredited college or university
• Currently enrolled in an MBA program for an MBA internship OR a Master's program for a Master's internship OR a PharmD program for a PharmD internship OR Ph.D. for a PhD internship from an accredited college or university and completion of the first year of MBA OR Master's OR Pharm D OR Ph.D. program before the internship starts

Preferred Qualifications

• Degree in Computational Chemistry, Cheminformatics, Computational Biophysics, Data Science or a similarly quantitative field
• Familiarity with machine learning models for molecular property prediction, including model development and evaluation of predictive performance
• Experience with molecular data processing and analysis using cheminformatics tools such as RDKit, and familiarity with model interpretability methods for exploring structure-property relationships
• Demonstrated programming and data analysis experience in Python, with familiarity with Unix-based systems and version control software (Git)
• Highly motivated; enthusiastic about science and problem solving
• Track record of research contributions through peer-reviewed publications or presentations at scientific conferences