Elman is building AI to predict clinical trial and drug development outcomes, and quantify how much confidence those predictions deserve. Our starting point is the evidence available when a decision is made, what we judge from it, and what happens next. By linking those records, we aim to learn which experimental findings predict outcomes in which therapeutic settings. The engine already gathers evidence and tests scientific explanations. We're looking for a technical co-founder to lead its development and work out how to measure and improve its predictive performance.
What we are working on. 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 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 has never systematically measured how far any given kind of evidence should carry. We want to measure how reliably different kinds of biological evidence predict human outcomes, and 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. There is no established dataset or method that settles this question. Defining what to measure, building reliable evaluations and choosing appropriate methods are central to the role.
Your 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. Responsibilities include developing the platform; building the measurement programme and identifying where the system is reliable and where it is not; preventing misleading evaluations by addressing leakage, label quality and contamination; hiring the first technical team once funding is in place; and supporting fundraising. 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.
What we are looking for. 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. Evaluation and benchmark design, including defining labels where they are not readily available and designing splits that prevent leakage. Applied statistics: 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, probabilistic graphical models and learned calibration, and the judgement to choose between them. Production LLM systems: 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. Hands-on technical leadership: you lead development and build the system yourself, working through AI coding assistants and automated pipelines and owning how we use them. Careful interpretation of results, and clear technical communication. Experience with biological or clinical data is useful but a biology background is not required. 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.
Stage. Elman is a pre-seed company spinning out of Deep Science Ventures. The platform is already running and has produced a scientific result through work with the Allen Institute, described in a public preprint at https://www.biorxiv.org/content/10.64898/2026.07.01.734821v1. The next funding round will fund the measurement programme. This is a co-founder role, with founder equity and technical authority from day one.
Apply with a short paragraph on what you would measure first and links to relevant work. No cover letter. Questions to hello@elman.ai.