Computational Chemistry Intern, Summer 2027

Genesis Molecular AI is bringing together a world-class computational team to build the industry’s fastest and most accurate small molecule property predictions by combining machine learning and physics-based methods. We are looking for early-career scientists who develop and apply computational chemistry and physics methods, increasingly with AI in the loop, to push forward our challenging drug discovery programs.

You will

Work with a dedicated mentor on a focused research project that matches your interests and expertise and addresses a key challenge in our internal and partnered drug discovery programs.
Develop and refine computational chemistry and physics methods. Potential project areas include quantum chemistry (DFT and wavefunction methods, QM/MM, torsional and conformational energetics, protonation and tautomer states), free energy simulations (relative and absolute binding free energies), and enhanced sampling (metadynamics, replica exchange, adaptive sampling).
Collaborate with our machine learning team on molecular modeling and property prediction. Potential projects include machine-learned interatomic potentials (MLIPs) and hybrid ML/MM simulations, learned molecular representations, learned collective variables for enhanced sampling, generative models of conformational ensembles, protein–ligand co-folding and binding affinity prediction, and ADME property prediction.
Work closely with our computer-aided drug discovery scientists and medicinal chemists to benchmark methods on real program data, test predictions prospectively, and integrate successful improvements into our drug discovery platform.

You are

A PhD student with a proven track record of applying or developing computational chemistry and physics methods. Exceptional MS or advanced undergraduate students with substantial research experience are also encouraged to apply.
Experienced in one or more areas of computational chemistry: quantum chemistry, free energy methods, enhanced sampling, molecular dynamics, or machine learning for molecular systems. Depth in one area matters more than breadth across all of them.
Passionate about building methods that solve real problems, and rigorous about testing assumptions and validating results.
A skilled programmer, preferably in Python, who can quickly turn research ideas into working code and assess the accuracy and reliability of the resulting methods.
Experienced using AI coding agents to accelerate your work, with the scientific and engineering judgment to verify generated code, debug subtle failures, and recognize when results are physically wrong.

What we offer

The opportunity to work on challenging, high-impact computational chemistry research aimed at accelerating the discovery of new medicines.
A world-class, tight-knit team of good-hearted people across software, machine learning, computational chemistry, medicinal chemistry, and biology.
A one-on-one mentor to guide your research and be your point person for questions.
An end-of-summer presentation of your work to the company.

About Genesis Molecular AI

Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes and GEN) with a total potential deal value of several billion dollars.

Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.