Tacit is an applied research lab at the intersection of AI and the life sciences.

Biology offers greater potential to improve human life than any other scientific domain. It's also the most complex — demanding measurement, integration, and reasoning across multiple scales, with hundreds of variables interacting simultaneously within systems we still only partially understand.

Nowhere is that complexity more apparent, nor the stakes more high, than in drug discovery and development. From identifying targets from human biology, to designing and validating interventions, conferring the right biophysical and pharmacokinetic properties, and ensuring that the result is reliable and reproducible at scale — every step requires solving a chain of deeply interdependent problems over noisy, heterogeneous data. It is, in many ways, the hardest thing biology asks us to do.

A snapshot of the current moment in time: general-purpose reasoning models are quickly approaching parity with the best human scientists; computational chemistry models are dramatically compressing research timelines by virtualizing and scaling experimental workflows; and sequencing costs are declining faster than Moore’s Law, enabling an abundance of biological data at increasingly granular resolution. We are, in other words, sitting at the advent of a golden age of scientific progress. In the limit, these forces will compress drug discovery to a function of data, compute, and token spend. The shape of company that will bring the next generation of drugs to market will look fundamentally different as a result — model-centric, massively parallelized, and increasingly autonomous. Tacit is working across the ecosystem to design the blueprint for this kind of company. We are a team of researchers, engineers, scientists, and operators who have trained frontier language models, designed and scaled experimental platforms, and brought drugs to the clinic.

We are actively hiring.