Member of the Technical Staff, Biological Data
About The Position
Output has built a biological reasoning model that understands biology at the scale and complexity life actually operates. Our model independently learned the principles of molecular interactions, opening up drug treatments that were previously impossible. We're already generating therapies that traditional approaches cannot reach. The hardest problems in both AI and biology are being solved here, and there is room for you to own one. Output is currently in stealth, operated by a team of repeat founders and biotech veterans with multiple exits in AI x Bio, and backed by top-tier VCs including Y Combinator. You accelerate the path from a generated molecule to a synthesized compound. This role continues to build the models and computational methods that optimize Output's molecular generation for practical chemistry. You will continue developing and training models that incorporate knowledge of chemical synthesis routes and reactions, steering molecular generation toward molecules that are efficient to synthesize You will build scalable computational tools and methods that evaluate synthetic feasibility across generated molecular libraries, systematically and at scale You will interpret model inputs and outputs in chemistry terms, translating between the language of generative AI and the language of synthesis You will work with the drug discovery and model teams, bringing chemistry expertise to molecular evaluation and candidate prioritization You will 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 post-doctoral or industry research experience, OR a Master's degree with 5+ years of hands-on experience in computational chemistry or cheminformatics
- Deep understanding of organic chemistry, synthetic routes, and chemical reactions
- Experience training machine learning models on molecular and chemical data, including generative models for chemistry applications
- Strong programming skills in Python, with experience building computational pipelines for molecular analysis
- Understand drug-like properties and medicinal chemistry principles, and how molecular structure relates to biological activity and synthetic feasibility
- Comfortable working at the boundary of chemistry and machine learning, translating constraints and insights between the two
Nice To Haves
- Experience with retrosynthetic analysis or computational synthesis planning
- Experience with molecular property prediction or QSAR modeling
- Publications at top-tier venues (e.g., NeurIPS, ICML, ICLR) or relevant chemistry and cheminformatics journals
- Drug discovery experience, particularly in hit-to-lead or lead optimization
- Experience evaluating or working with generative molecular models
Responsibilities
- Continue developing and training models that incorporate knowledge of chemical synthesis routes and reactions, steering molecular generation toward molecules that are efficient to synthesize
- Build scalable computational tools and methods that evaluate synthetic feasibility across generated molecular libraries, systematically and at scale
- Interpret model inputs and outputs in chemistry terms, translating between the language of generative AI and the language of synthesis
- Work with the drug discovery and model teams, bringing chemistry expertise to molecular evaluation and candidate prioritization
- Build and maintain cheminformatics pipelines for molecular analysis, property calculation, and candidate assessment
Benefits
- Competitive salary and equity in a growing, well-funded startup
- Excellent medical, dental, and vision coverage
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What This Job Offers
Job Type
Full-time
Career Level
Senior
