
Job Description
Genesis Molecular AI is building a world-class software team to solve problems in drug discovery through machine learning, biophysical simulation, and computational chemistry. We are looking for engineers excited to help develop new medicines and play a critical role in building out our software platform.
You will
Build and expand on tools for visualizing molecules and proteins, analyzing machine learning models, and managing complex chemical workflows
Work closely with machine learning engineers and medicinal chemists to develop and productionize new computational methods for molecular property prediction
Help scale our core infrastructure to handle billions of datapoints and thousands of parallel deep learning and molecular dynamics jobs
You are
A strong software engineer who will write code quickly and uphold our code quality standards
A deep thinker who reasons from first principles over pattern matching
Interested in learning about biochemistry and the drug development process
What we offer
The opportunity to work on high impact tools and products that are immediately deployed to accelerate the discovery of new medicines
Strong technical coworkers in AI, software, and chemistry, who all have a powerful mix of intellectual curiosity and humility. The team reads and discusses 1-2 machine learning or chemistry papers every week to stay on top of the field and inspire new ideas
Application & Hiring Timeline
Application Deadline: April 24, 2026
Resume Review: Beginning May 4, 2026
Interviews Kick-Off: May 11, 2026
About Genesis Molecular AI
Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. The company’s 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 has raised over $300 million from leading AI, tech and life science-focused investors, signed multiple AI-focused research collaborations with major pharma partners, and is deploying GEMS to advance an internal therapeutics pipeline for a variety of high-impact targets.
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.
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Job Details
- Category
- Software
- Employment Type
- Internship
- Location
- San Mateo, CA
- Posted
About Genesis Molecular AI
Genesis Molecular AI – headquartered in Burlingame, CA, with a fully integrated laboratory in San Diego and offices in New York – is pioneering foundation models for molecular AI to unlock a new era of drug design and development. We are using a proprietary state-of-the-art generative and predictive AI platform called GEMS (Genesis Exploration of Molecular Space), to accelerate and optimize small molecule drug discovery. The GEMS platform integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl for structure prediction. GEMS accelerates hit ID through lead optimization and candidate selection by generating promising molecules for synthesis and experimental testing, and iterating this process through cycles of AI-enabled discovery and optimization. We have leveraged GEMS to build an internal pipeline with multiple programs against high-value targets, including data-poor and canonically undruggable targets where GEMS is uniquely advantaged. In addition, Genesis has signed AI platform collaborations across a range of therapeutic areas including Gilead (2024), and Incyte (2025). Genesis has raised over $300M in funding from top AI, technology and biotech investors, including Andreessen Horowitz, Rock Springs Capital, T. Rowe Price, Fidelity, Radical Ventures, NVentures (NVIDIA's VC arm), BlackRock, and Menlo Ventures. To learn more about Genesis Molecular AI, or current employment opportunities, please visit our website.
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