Member of Scientific Staff, Computational Chemistry

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About the role

LOCATION: King’s Cross, London · PATTERN: Hybrid, flexible for the right candidate The opportunity We’re a stealth startup in AI and bio, building infrastructure for data generation. The team is small and elite, the problems are hard, and the foundations are being laid right now. You’d help write them from the first line, with real ownership and a direct line to the founders. This role builds our computational chemistry from the start. You would own how a set of target molecules becomes a synthesis plan that automated equipment can run, and how every compound we work with is represented and registered. About us AI for biology has a data problem. The data that matters most doesn’t exist yet, so we’re building the infrastructure to produce it, with quality and traceability built in from day one. We’re in stealth and heads-down on execution. We’ll share more about what we’re building once you’ve spoken with the team. The role You will own computational chemistry end to end, from the molecules someone wants made to the plan the lab executes. The role sits at the interface between synthetic chemistry, cheminformatics and software, and lab automation, and you will build the computational capability alongside the physical synthesis and automation capability from an early stage. The role is embedded with the synthetic chemists and the automation scientists and engineers, and you will work with them every day. Much of the value comes from drawing out what experienced chemists know and turning their judgement into rules, models and workflows that apply the same way every time. Two parts of the role go beyond standard computational chemistry, and they are why it matters so much. Routes that automation can execute. A chemically plausible route is only the starting point. Planning has to account for what the platform can do, including the building blocks and reagents available, the reaction classes and conditions it supports, handling constraints, work-up and purification, and throughput. It also has to work across a whole target set. Given hundreds or thousands of targets, you will find the shared chemistry, common intermediates and building blocks, and reduce them to a manageable set of synthetic strategies, balancing diversity and compound value against synthetic cost. You will work with the automation team to define what planning hands to the systems downstream, and over time connect planning, synthesis and experimental results so that each round of results improves the next plan. Chemical identity from the outset. The digital representation and identity of compounds has to be right from the start, because other scientific teams will rely on compound registration for chemical identity, as well as synthesis planning. You will establish when two structures represent the same chemical entity and when they do not, and handle stereoisomers, salts, tautomers, protonation and charge states and other representation and normalisation questions consistently. You will be the first person in this seat. You will choose the methods, set the standards, write the first production code and shape how the capability grows, including who joins it. Your first 90 days FIRST 30 DAYS ◆ Learn how the synthetic chemists and the automation team work, and what the synthesis platform will and will not support. ◆ Agree the rules for chemical identity with the chemists, covering structure normalisation, validation and deduplication, and how stereochemistry, isomers, salts, tautomers and charge states are handled. ◆ Start ingesting vendor catalogues and building-block inventories through supplier APIs and external chemical data sources, capturing availability, cost and metadata so they can act as planning constraints. ◆ Agree with the automation team what a synthes

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