Co-founder & CEO, AI for predictable drug discovery and development

The Opportunity

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 it still fails. We don't think the fix is more of the same. Brute force hasn't worked, throwing more compute and more data at biology hasn't made it predictable. Making today's process a little faster won't work either, not at any real scale, because it would still be capped by the scale of what human teams can hold in their heads. What will work is a different way of doing the science itself: an AI framework that reasons through it end to end, drawing on everything, the literature, data and the wet-lab, built to get the most out of what today's agentic models are great at while guarding hard against where they fail.

The proof-of-concept is done and has been tested on multiple use cases. In a single run it processed an entire disease's single-cell data, ingested hundreds of experimental facts extracted from literature, built a connected map of the biology, and returned drug-target hypotheses. Each of those was marked supported or refuted, and is 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, pre-print can be found here: here. The current system already produces reliably strong results for certain parts of the drug development pipeline, such as target validation, powered by the data modalities we've built in. The plan is to scale that to every modality to 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.

The mission: not to make today's process a bit faster, but to change what's possible.

THE ROLE

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 cofounders, who own the engine and the science. You will:

• Shape strategy and positioning alongside the founding team
• Lead fundraising: run a competitive seed round and the path to Series A
• Build the commercial engine: partnerships, pilots and paying customers across pharma and biotech
• Build the company: hiring, story, board, grant and academic partnerships, and day-to-day operations

Location

Hybrid, UK-based

Commitment

Full-time from spin-out

Equity

Milestone-linked so that delivering a strong round is richly rewarded

Essential Values

• Driven to build a category-defining company at the frontier of AI and drug discovery, and to challenge how the industry works today
• Impact-driven: you take the initiative, make things happen, and think from first principles about what's really needed
• Clear entrepreneurial spirit: with the ability to thrive in an ambiguous, unstructured, fast-paced and demanding environment
• Collaborative by nature: able to partner closely with both the product and tech cofounders

Essential Experience (must-have)

• World-class expertise in a related technical field (AI, ML, therapeutics, etc.)
• Either you've personally led a priced round, or your name and track record make one raisable
• US or UK based (or flexible on moving) and able to go full-time from spin-out

Preferred Experience (nice-to-have)

• Experience in a leading AI lab or AI-drug-discovery company in a genuinely impactful role
• A strong, transferable investor network in deep-tech, AI, bio or health
• Experience selling technical or scientific software into pharma, biotech or research institutions
• Experience scaling a company from seed to Series A and beyond