Cheminformatics and Machine Learning Intern at Genesis… · New York

Summary: Genesis Molecular AI is bringing together a world-class computational team to build out the industry's fastest and most accurate small molecule property predictions. They are seeking early career scientists to develop and apply machine-learning and cheminformatics methodologies to drive forward drug discovery programs. Responsibilities: - Lead a novel research project from ideation to conclusion, focused on improving our internal tools for potency and ADME prediction - Prototype novel approaches from recent publications. Design and execute large-scale experiments to validate promising avenues, using internal and public benchmarks - Work closely with our computer-aided drug discovery scientists and medicinal chemists to develop, benchmark, and deploy improvements to our drug discovery platform as it is applied to our internal and partnered drug discovery programs Required Qualifications: - A graduate student with a proven track record of developing cheminformatics tools and/or physics methods in contexts relevant to drug discovery - Experienced Python programmer with proven ability to navigate and contribute to complex codebases - Proficient ML practitioner, familiar with common architectures (understanding their strengths and tradeoffs) and with proven expertise in troubleshooting real-world applications - A detail-oriented data scientist skilled in managing diverse data sources - Familiarity with RDKit, Openeye and other cheminformatics libraries is a plus - Passionate about making a direct impact on drug-discovery programs and interacting with a diverse team of ML practitioners, medicinal chemists and drug-discovery scientists Required Skills: Cheminformatics, Machine Learning, Python Important Skills: Data Science Benefits: Dedicated mentorship from a senior researcher on our team who will partner with you, guide your project, and champion your growth., A world-class, mission-driven team of good-hearted people across software, machine learning, computational chemistry, medicinal chemistry, and biology.