Output Biosciences
Generative AI that speaks biology
About Output Biosciences
Funding & Growth
✓ Pros
- • Generative AI for biology is a fast-moving research frontier
- • Small team — direct ownership and influence
- • NYC location with some remote roles
- • Modern ML stack and serious compute investment
✗ Cons
- • Early-stage uncertainty (Series A startup risk)
- • Small team means narrower role focus may not always match interests
- • Compensation transparency is limited at this stage
🏢 Working Here
Output Biosciences is an early-stage (Series A) generative AI for biology startup in New York City.
The company applies modern generative ML — including transformer and diffusion architectures used for language and image generation — to the problem of designing novel biological sequences and predicting their function.
Early-stage means small team, high individual ownership, and direct interaction with leadership.
The work sits at the intersection of foundation-model research and wet-lab biology.
🧬 Bioinformatics Focus
Output's computational work spans generative model training on biological sequence data (DNA, RNA, protein), evaluation of designed sequences using in-silico predictors before wet-lab validation, building infrastructure for high-throughput compute on GPU clusters, and protein/RNA design for therapeutic candidates.
Strong ML engineering and research skills are weighted at least as heavily as traditional bioinformatics — PyTorch, JAX, transformer architectures, and large-scale training pipelines are common requirements.