USD per year
The Role
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
About You
You have a 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 You have deep understanding of organic chemistry, synthetic routes, and chemical reactions You have experience training machine learning models on molecular and chemical data, including generative models for chemistry applications You have strong programming skills in Python, with experience building computational pipelines for molecular analysis You understand drug-like properties and medicinal chemistry principles, and how molecular structure relates to biological activity and synthetic feasibility You are comfortable working at the boundary of chemistry and machine learning, translating constraints and insights between the two
Bonus Points
You have experience with retrosynthetic analysis or computational synthesis planning You have experience with molecular property prediction or QSAR modeling You have publications at top-tier venues (e.g., NeurIPS, ICML, ICLR) or relevant chemistry and cheminformatics journals You have drug discovery experience, particularly in hit-to-lead or lead optimization You have experience evaluating or working with generative molecular models
Our Values ❤️
Heart: We foster a culture of ownership. We are assembling a team of individuals who are passionate and take pride in their contributions. Excellence: We have an unwavering commitment to excellence and continuously challenge ourselves to reach the highest standards. Practicality: We focus on results over process. Honesty: We communicate directly. Creativity: We encourage contrarian thinking. Openness: We welcome feedback. Leadership: We support each other.
Location & Employment Type & Compensation Details
Location: New York HQ 🗽; Bay Area Employment Type: Full time Compensation Range: $150K - $350K plus equity
We’re a fully integrated AI lab building Artificial General Biological Intelligence (AGBI), from the molecular level up to whole systems. We’ve built mechanistic reasoning models where reasoning over biological interactions at the molecular level emerges from molecules alone. The discovery of new biology and design of therapeutic moonshots become routine.
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