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

We're building AI to predict clinical trial and drug development outcomes, and quantify how much confidence those predictions deserve.

We're looking for a technical co-founder to join our biology and commercial founders. As Co-founder & CTO you take a built, already-used platform and lead its development, the research programme that measures and improves its predictions, and the first technical team. The aim is to spin the company out of DSV over the coming months.

THE OPPORTUNITY

Drug development involves decisions about targets, treatment approaches, delivery and patient populations. Each decision depends on evidence that may not transfer well to the setting that matters: treating people. A treatment working in mice, for example, does not tell us how much confidence to place in its chances in humans.

Around nine in ten drugs entering clinical trials never reach approval. The causes run from efficacy to safety to commercial choice, and the claim here is narrower than blaming that number on translation: the field still lacks a systematic way to measure how far a given experimental result should carry into a specific therapeutic decision.

We want to measure how reliably different kinds of biological evidence predict human outcomes. Our approach is to link the evidence available when a decision was made, the judgement made from it, and what happened next. Those records become the data from which we can learn which evidence predicts which outcomes, and under what conditions. That is difficult to test. Outcomes can take years to arrive, suitable labels are often missing, and a failed trial does not necessarily reveal which earlier assumption was wrong.

Values

• Driven to build a category-defining company at the frontier of AI and drug development, 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 and demanding environment.
• Collaborative by nature: able to partner closely with a founding team that holds the biology and the commercial side.

Experience (must-have)

• Evaluation and benchmark design. Experience defining labels where they are not readily available, checking their reliability and designing data splits that prevent leakage.
• Applied statistics. A strong understanding of calibration, confidence intervals, base rates and the limits of small or noisy datasets.
• Breadth across modelling approaches. Hierarchical and Bayesian inference, classical statistics, graph learning including graph neural networks, probabilistic graphical models and learned calibration. We care less about depth in any one of these than about how you decide between them on a given dataset.
• Production LLM systems. Experience with agent orchestration, tool integrations, context management and evaluation systems that catch regressions. The platform is model-agnostic and routes across Anthropic, OpenAI, Google and specialist models, so experience running more than one provider in production is useful.
• Hands-on technical leadership. You lead development and build the system yourself. AI coding assistants and automated pipelines run through most of our work. You would own how we use them, and be responsible for reviewing and testing what they produce.
• Careful interpretation of results. You report uncertainty and limitations, and recognise when the available evidence is insufficient to support a conclusion.
• Clear technical communication. You can explain and defend your decisions to collaborators and technical investors.
• Able to go full-time from spin-out. Location is flexible. The company is UK-based and we prefer candidates here or willing to relocate, but we already work distributed and remote is workable for the right person.
• Not building, and not recently building, a company on the same substrate: agentic reasoning, knowledge graphs and hypothesis generation for drug discovery.

Preferred experience (nice-to-have)

• Experience with biological or clinical data. A biology background is not required.
• Previous founding experience.
• Research, software or open-source work we can review.
• Time at a frontier AI lab or a top AI-for-science company, in a role the field would recognise by name.
• Experience building evaluation or benchmarking infrastructure that other people then relied on.