Computational Chemist (AI)

TL;DR

Computational Chemist (AI) (Molecular Generation/Cheminformatics): Developing models and computational methods that guide molecular generation toward compounds that are practical to synthesize, with an accent on chemical reactions, synthetic feasibility, and molecular evaluation. Focus on training generative models, scaling cheminformatics pipelines, interpreting model hirify.globals through chemistry, and prioritizing drug discovery candidates.

Location: New York headquarters or the Bay Area, United States

Salary: $150,000–$350,000 plus equity

Company

Stealth biotech startup combining biological reasoning models, generative AI, and computational drug discovery to develop therapies beyond the reach of traditional approaches.

What you will do

• Develop and train models incorporating chemical synthesis routes and reactions to improve molecular generation.
• Build scalable tools for evaluating synthetic feasibility across generated molecular libraries.
• Interpret model inputs and hirify.globals in chemical terms and translate between generative AI and synthesis concepts.
• Collaborate with drug discovery and modeling specialists on molecular evaluation and candidate prioritization.
• Build and maintain cheminformatics pipelines for molecular analysis, property calculation, and candidate assessment.

Requirements

• PhD in chemistry, computational chemistry, cheminformatics, or a related field with 2+ years of postdoctoral or industry research experience, or a master’s degree with 5+ years of hands-on experience.
• Deep knowledge of organic chemistry, synthetic routes, and chemical reactions.
• Experience training machine learning models on molecular and chemical data, including generative chemistry models.
• Strong Python programming skills and experience building molecular analysis pipelines.
• Knowledge of drug-like properties, medicinal chemistry, and the relationship between molecular structure, biological activity, and synthetic feasibility.
• Ability to work at the intersection of chemistry and machine learning.

Nice to have

• Retrosynthetic analysis or computational synthesis planning experience.
• Molecular property prediction or QSAR modeling experience.
• Publications in leading machine learning or chemistry and cheminformatics venues.
• Drug discovery experience in hit-to-lead or lead optimization.
• Experience evaluating generative molecular models.

Culture & Benefits

• Ownership-driven culture with high standards and a focus on meaningful patient impact.
• Open, direct, and constructive feedback environment.
• Encouragement of creativity, contrarian thinking, and new ideas.
• Autonomy over day-to-day work with a focus on achieving milestones.
• Competitive compensation with equity at a well-funded startup.
• Medical, dental, and vision coverage.