Job Description

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.

The platform is already running. It has produced a scientific result through work with the Allen Institute, described in a public preprint. You can also see the engine run.

There is no established dataset or method that settles the measurement question. Defining what to measure, building reliable evaluations and choosing appropriate methods are central to the role, and the next funding round will fund that programme.

Clinical trial outcome prediction is a central test: predicting a specific trial outcome and explaining the biological reasons, using evidence available before the result. An LLM may already know a historical outcome, so retrospective tests need safeguards against contamination as well as confirmation on prospective cases. This article sets out our approach.

The aim is to make therapeutic development more predictable, and use those predictions to change which treatments get developed and how.

Read more in our thesis.

THE ROLE

This is a deeply hands-on role. The founding team covers biology and commercial development. You will be responsible for the technology and the research needed to evaluate it. There is no engineering team yet: you will be building the system yourself and should expect to remain hands-on for at least the first year. You will:

• Develop the platform. Take ownership of the existing system, set technical priorities and implement improvements.
• Build the measurement programme. Define the prediction tasks, link evidence available at decision time to subsequent outcomes, and develop and compare modelling approaches. Test predictive performance and calibration, and identify where the system is reliable and where it is not.
• Prevent misleading evaluations. Address leakage, label quality and contamination. Retrospective tests need particular care when an LLM may have encountered the outcome during training. We explain the issue in this article.
• Hire the first technical team once funding is in place.
• Support fundraising. Explain the technical approach, evidence, limitations and development plan to investors.

The modelling approach is open. You will choose methods based on the problem and the available data, whether those involve Bayesian or hierarchical inference, graph learning, calibration, classical statistics or a combination.

Location: Flexible, UK-based preferred · Commitment: Full-time from spin-out · Equity: Founder equity, with technical authority from day one.

WHO WE'RE LOOKING FOR

We're building an AI company that works on drug development, not a drug company that uses AI. The hard part is measuring what the evidence justifies, and biology is where we point that. So the depth we're searching for is in AI and statistics.

You have led a team or a substantial technical programme at Google DeepMind, OpenAI, Anthropic, Meta AI or a comparable frontier AI lab. Your own work is recognised, and your name opens doors with investors and people we want to hire. That credibility is part of why this role exists. You remain hands-on and will build the platform yourself.

You might know very little biology, and that's okay. The science is held by the founding team. What we look for is real curiosity about the biology and the drive to learn it quickly from the people who already know it.

You will be building the platform yourself. With the rest of the founding team you set what it is for and where it goes, and then you make it work.

These are essential:

• You bring a recognised technical track record from a frontier AI lab and remain hands-on.
• You hold your own in a room of researchers and a room of investors.
• You lead from the front. This is a founding seat, not a board seat and not an advisory one.
• You can go full-time from the spin-out.

HOW TO APPLY

Send a CV and a short note. In the note, tell us what you would measure first, and what you think we've got wrong about the mission above. Include links to work you have built or published. No separate cover letter is required.

One request, and we ask it of everyone. If your application reports a result (an accuracy, an improvement, a benchmark number), be precise about three things: what the baseline was, how the data was split, and how wide the interval around it is. A figure on its own tells us nothing.

If a founding seat isn't the right fit but the problem pulls at you, we're also open to advisory roles; say so in your note.

Requirements

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.
• Experience building evaluation or benchmarking infrastructure that other people then relied on.

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.

• A genuine co-founder equity stake.
• A working platform and a published scientific result already in place, so the first thing you build is the measurement programme.
• Technical authority from day one, architecture included.
• Access to DSV's global network of investors, advisors and industrial partners.
• Access to proprietary venture-building tools, resources and processes proven to create high-impact companies from scratch.
• Continuous post-spinout support including fundraising, commercial partnerships, recruitment and team-building.

ABOUT DSV

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. Elman is spinning out of its Pharmaceuticals practice.