Drug discovery is broken. We're building the AI that fixes it: a closed-loop agentic system that does the scientific reasoning end to end and returns predictions you can trace back to the evidence behind them.
We're looking for a founder and operator to join our technical and product founders. As Co-founder & CEO you take a built, already-used platform from spin-out through a competitive seed round and on to Series A. The company spins out of Deep Science Ventures over the coming months.
Drug discovery is one of the most important problems in the world and one of the least solved. Biology is complex; the useful data is sparse; the work runs in slow cycles of trial and error. Teams spend years and billions of dollars on development, and most of the time the drug still fails.
Brute force will not fix it: more compute and more data have not made biology predictable. Making today's process incrementally faster will not either, because that process is capped by what a human team can hold in their heads. What works is a different way of doing the science itself, an AI framework that reasons through it end to end, drawing on the published literature and the data and closing the loop with the wet lab. It's built around what today's agentic models are good at, and guarded hard where they fail.
The proof of concept is ready and has been tested on several use cases. In a single run it processed an entire disease's single-cell data, ingested hundreds of experimental facts extracted from the literature, built a connected map of the biology and returned drug-target hypotheses. Each hypothesis came back marked supported or refuted, traceable with provenance to the exact evidence behind it. When the data is this thin, that grounding is what separates an answer you can build on from one you take on faith. The work came out of a collaboration with the Allen Institute, and the first results are submitted to a top-tier journal (Cell). Preprint: https://www.biorxiv.org/content/10.64898/2026.07.01.734821v1.
The system already produces strong results for parts of the drug development pipeline, target validation among them, powered by the data modalities we have built in. The plan is to scale that to every modality and reach what the whole field is chasing: reliably predicting what a clinical trial will do before it runs, with a concrete explanation of the biology of why.
We prove things with hard, contamination-free benchmarks rather than decks. We've built a freedom-to-operate benchmark for our internal tool and are working towards a clinical-trial benchmark with mechanistic explanation, because a trial is the one place biology gives a straight answer. An engine validated against that benchmark is more than capable of tackling every stage of drug development, and in time, of showing not just what a trial will do but how to change it.
That's the mission. Not to make today's process a bit faster, but to change what is possible.
This is a deeply hands-on role. As Co-founder & CEO you own the commercial and company-building side from day one, working hand in hand with the other founders, who own the platform and the science between them. You will:
Location: Hybrid, UK-based · Commitment: Full-time from spin-out · Equity: Meaningful co-founder equity, milestone-linked.
We're building an AI company that works on drug discovery, not a drug company that uses AI. The hard part is the reasoning system, and biology is where we point it. So the person we're searching for has their depth in AI.
You've held a role with real ownership at one of the labs or companies at the front of the field, and the work you did there is known by name. Or you've built a company and raised the money for it yourself, on your own conviction. Either way, investors back you on your record alone.
You might know very little biology, and that's okay. The science is held between the two founders. What we look for is a real curiosity about the biology, and the drive to learn it quickly from the people who already know it. You won't be building the platform; with the rest of the founding team, you'll set the strategy for what it's for and where it goes.
If your depth is in the science rather than AI, and your name alone raises a round, everything else here still applies. Get in touch.
Either way, these are essential:
Requirements
Values
Experience (must-have)
Preferred experience (nice-to-have)
Send a CV and a short note. In the note, tell us what you think we've got wrong about the mission above.
One request, and we ask it of everyone. If your application says you raised money, be precise about five things: which fund, which round, whether that fund led it, your title on the day it closed, and what became of the company. A figure on its own tells us nothing, because it never distinguishes the money you raised from the money the company had raised while you worked there. We check.
If a founding seat isn't the right fit but the mission pulls at you, we're also open to advisory roles; say so in your note.
Benefits
By joining DSV, you'll be joining a team of operators who have founded companies and led the translation of science at some of the most respected universities, charities, funds and government agencies. DSV is a leading deep-tech venture studio with a portfolio of 50+ science-led companies at a total valuation of ~$700m.
Deep Science Ventures (DSV) is on a mission to create a future in which both humans and the planet can thrive. We use our unique venture creation process to create, spin-out, and invest in science companies, combining available scientific knowledge and founder-type scientists into high-impact ventures. Operating across Pharmaceuticals, Climate, Agriculture, and Computation, we tackle the challenges defining these areas by taking a first-principles approach and partnering with leading institutions.