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Genesis Molecular Ai

43 Genesis Molecular Ai Jobs Hiring Near You

Genesis Therapeutics

Product Management Lead

Manhattan, NY · On-site

$120 - $190/hr

Genesis Molecular AI is pioneering a transformative approach to drug discovery by leveraging state-of-the-art machine learning, computational chemistry, and biology. Our mission is to accelerate the ...

Genesis Therapeutics

Product Management Lead

San Diego, CA · On-site

$120 - $190/hr

Genesis Molecular AI is pioneering a transformative approach to drug discovery by leveraging state-of-the-art machine learning, computational chemistry, and biology. Our mission is to accelerate the ...

Genesis Therapeutics

Product Management Lead

San Mateo, CA · On-site

$120 - $190/hr

Genesis Molecular AI is pioneering a transformative approach to drug discovery by leveraging state-of-the-art machine learning, computational chemistry, and biology. Our mission is to accelerate the ...

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Genesis Molecular Ai Jobs Information

Infographic showing various job openings at Genesis Molecular Ai in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 71% Physical, and 29% Remote job distribution.

Applied ML Scientist (Staff / Principal)

Genesis Molecular AI

San Mateo, CA • On-site

Full-time

This job post has expired 3 days ago. Applications are no longer accepted.

Job description

Job Summary:
Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. The role involves applying cutting-edge AI to solve real-world drug discovery challenges and serves as a critical bridge between long-term research and experimental drug discovery programs.
Responsibilities:
• Work directly with project teams to assess model performance and utility, including applicability to current project needs, and collaborate with ML and engineering teams to resolve issues or add new functionality.
• Assist experimental colleagues with use and interpretation of model predictions by providing context about model quality and prediction uncertainty.
• Evaluate model quality by validating predictions against project data and internal or external benchmarks.
• Curate internal and external datasets for model training and validation (in collaboration with experimental teams).
• Contribute to design and analysis of experiments on model changes and alternative architectures.
Qualifications:
Required:
• A seasoned computational scientist with a proven track record of machine learning based methods to impact small molecule drug discovery projects.
• A cheminformatics expert, fluent in the language of molecular data with hands-on mastery of tools like RDKit or OpenEye.
• A scientist who speaks the language of experimental drug discovery, with a strong familiarity with common assay types (biochemical/binding/cell-based assays, in vivo studies, etc.) and CADD workflows (docking, virtual screening, ADME prediction, etc.).
• A rigorous data scientist, with experience in modeling and analysis of small molecule datasets and passion for statistical validation, uncertainty quantification, and deriving clear insights from complex, noisy data.
• A hands-on applied scientist and software engineer with strong coding skills in Python and a deep practical knowledge of the applied ML toolkit (e.g., scikit-learn, PyTorch).
• An exceptional communicator and collaborator, able to act as the bridge between machine learning researchers and experimental scientists.
• A curious, problem-oriented mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries.
• A true team player who thrives in highly collaborative, mission-driven environments where science and engineering are deeply intertwined.
• Inspired by our culture of intellectual curiosity and the shared belief that breakthroughs happen when diverse perspectives and minds unite.
Preferred:
• A PhD in Cheminformatics, Computational Chemistry, Computer Science, or a related field.
• A track record of publications applying machine learning to drug discovery challenges.
• Deep expertise in advanced modeling techniques such as graph neural networks, multitask modeling, active learning, or Bayesian optimization.
• Experience with large-scale data management, including SQL databases and data pipelining tools.
• Strong opinions on molecule featurization and model validation.
Company:
Genesis Therapeutics unifies AI and biotech to accelerate the discovery of new medicines. Founded in 2019, the company is headquartered in South San Francisco, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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