Phyla Tech is a founder-led team that builds and operates machine learning infrastructure for biotech. Our roots are in natural-products science, and the way we work still shows it.
Phylum is the rank Linnaeus's successors used to carve the living world into its great branches, between kingdom and class. Naming a thing, placing it, seeing what it's related to: that is the oldest move in biology, and it is still a useful one.
We came to machine learning through that tradition, not around it. Before there was a company, there was work on the chemistry of plants, fungi, and microbes, the field called pharmacognosy, the study of medicines drawn from the living world. Underneath every model we ship is the same instinct: sort the data, find the structure, name what's there.
Between us we came up through computational drug discovery, metagenomics, multi-omics, and the software engineering that holds those together. We spent years building the pipelines, models, and tools that research actually runs on, then watched too much of that work stall because the infrastructure around it was an afterthought.
Phyla Tech exists to be that infrastructure, done properly. We take the parts of a biotech's data and ML stack that need to be reproducible, observable, and fast, and we build and operate them like the production systems they are. The science is the point; the engineering is how it survives contact with reality.
We work as one team, end to end. The people who scope an engagement are the people who build it. There are no layered handoffs and no junior delivery, and there is no version of us that hands you a slide deck instead of a working system.
Chemistry, biology, mathematics, and the engineering that connects them. These are the people you'll work with, and everyone here does the work.
Background in biochemistry and computational drug discovery. Builds the model training and deployment infrastructure at the spine of Phyla Tech's engagements, the systems that take a model from a notebook to something a team can rely on in production.
Background in chemistry, bioinformatics, and computational drug discovery. Owns the bio- and cheminformatics side of the work, from molecule to genome: the pipelines and analysis that turn raw scientific data into something you can build on.
Background in mathematics and software engineering. Builds the applications, dashboards, and tooling that turn scientific systems into something a team can actually use day to day.
A pipeline that needs to be reproducible. A model that needs to ship. A dataset nobody can see clearly yet. That's the work we want.