About the job
Cheminformatics and Machine Learning Intern
Genesis Molecular AI is assembling a premier computational team dedicated to creating the industry’s most rapid and precise small molecule property predictions by harnessing the synergy of machine learning and physics-based methods. We invite early-career scientists who excel in the development and application of machine learning, physics, and cheminformatics techniques to advance our ambitious drug discovery initiatives.
About the Role
In this role, you will be an integral part of our machine learning and cheminformatics team, contributing significantly to our internal ML and physics-based platform that propels our drug discovery projects forward.
Your responsibilities will likely encompass developing innovative methodologies for potency and ADME/PK property prediction, as well as prototyping integrations with cutting-edge molecular modeling tools.
Your Responsibilities
- Lead a groundbreaking research project from concept to implementation, aimed at enhancing our internal tools for potency and ADME prediction.
- Prototype innovative approaches based on recent academic publications. Design and conduct large-scale experiments to validate promising findings using both internal and public benchmarks.
- Collaborate closely with our computer-aided drug discovery scientists and medicinal chemists to develop, benchmark, and implement enhancements to our drug discovery platform as it relates to our internal and partnered drug discovery efforts.
Your Profile
- A graduate student with a strong track record in developing cheminformatics tools and/or physics methods relevant to drug discovery.
- Proficient in Python programming with a demonstrated ability to navigate and contribute to complex codebases.
- An experienced machine learning practitioner with familiarity in common architectures and proven skills in troubleshooting real-world applications.
- A detail-oriented data scientist adept at managing diverse data sources. Familiarity with RDKit, OpenEye, and other cheminformatics libraries is a plus.
- Passionate about making a tangible impact on drug discovery projects and engaging with a diverse team of ML practitioners, medicinal chemists, and drug discovery scientists.

