AI Engineering Lead

Job description

The opportunity Substrate is building a network of fully autonomous wet labs, cloud-based data production facilities for AI biology, integrated with foundation models to become the critical infrastructure layer for AI-driven biological discovery. Our first node opens in King’s Cross, London, with several integrated workcells and two scientific verticals online by mid-2027. Our customers range from foundation model labs to global pharma. We are hiring an AI engineering lead as the first engineering hire on the intelligence software product. Substrate runs two AI products on top of operational data today, with more to come. You will write much of the first production code, help shape the architecture, and influence how the team that comes after you is built. About Substrate Substrate is spinning out of Automata, the UK lab automation company that has built the workcell platform our labs run on. Our four co-founders are Mostafa ElSayed (CEO and founder of Automata), Oli Hoy (formerly VP Customer Experience at Automata), Alexey Morgunov (AI Scientist co-founder, leading the intelligence software product), and a founding biology lead joining shortly. We are aiming to have ramped up to 32 people by the end of Q1 2027. We are funded in parallel by a combination of venture funding and government grants. We are not a cloud lab and we are not a CRO. We are an autonomous lab platform with closed-loop integration available as one operating mode for foundation model partners. The role You will sit alongside Alexey on the intelligence software product. You will write most of the early production code, help shape the architecture, and influence the engineering culture as the team grows. The product has two surfaces today and will grow over the next year. AI Scientist is an agent that ingests the scientific literature, identifies inconsistencies and high-value gaps in published data, and flags experiments where Substrate’s reserved R&D capacity could resolve open questions. AI Assays is a continuous-improvement product that uses run metadata to optimise assay protocols over time, reducing variable cost and increasing throughput. Both products run on top of an autonomous wet lab that is generating its own structured operational data from day one. You will work closely with Alexey on technical direction, with the founding software engineer on the boundary between operational…