Molecular Characterisation Scientist, Biologics
Role Summary
Join Substrate Bio as a Molecular Characterisation Scientist in London. Lead the development of automated biological data generation processes.
About the Organisation
Substrate is building the critical infrastructure layer between AI and biology: an AI-native automated lab that produces biological data at scale. AI for biology has a data problem, not a compute problem. Biological foundation models can predict but cannot experiment, and the high-quality, large-scale data they need does not exist. Substrate generates it, with quality and provenance built in. We are not a CRO and we are not a cloud lab.
The company was founded by four co-founders and is funded through a combination of equity and debt. The first lab is in London, with a larger automation node to follow. The work starts with two scientific verticals, protein characterisation and functional genomics, and this role sits at the heart of the protein characterisation work.
Most characterisation roles like this inherit an established assay menu and run it. This one does not. No assay in this vertical is being retrofitted onto automation; every protocol is designed for AI-in-the-loop execution from the first manual run. The data each assay produces, its output structure, metadata, provenance, and consistency across runs, is treated as a first-class scientific constraint, because that data feeds directly into foundation-model training pipelines. Your decisions at the bench affect what the orchestrator has to do and what data leaves the building.
Some scientists find this energising; others find it outside the lane they trained for. It is worth knowing in advance which one you are.
Minimum Requirements
We are hiring across three tiers: Lab Technician, Scientist, and Principal Scientist. The work is hands-on bench science at all levels, with collaboration into automation and software; the difference is depth of ownership, design authority, and responsibility. We do not hire peopl into boxes, and the early team stretches beyond the strict edges of any role.
- You are a protein scientist who is excited about the actual work: designing, validating, an running biophysical and developability assays at the bench. You are comfortable in the details.
- You have hands-on experience collecting and analysing binding kinetics and affinity data, and assessing the developability of biologics. The shape of the problem is what attracts you: assays designed for autonomous execution from day one, in a business where the data the lab produces is itself the product.
- You write good SOPs, and you hold yourself and your colleagues to clear reproducibility thresholds.
- You are pragmatic about being hands-on in the early phase, when the cadence is heaviest, and you understand it eases as protocols move onto instrumentation.
- You enjoy working at the boundary with non-biologist colleagues (automation engineers, software engineers, AI researchers), and you do not require them to be scientifically fluent before you will collaborate.
- Direct experience moving biophysical or analytical assays from manual workflows onto automation platforms (at least a handoff into an automated platform).
- Experience developing a tiered developability or characterisation cascade, mapping properties (binding, stability, aggregation, hydrophobicity, charge, polyreactivity) onto an assay funnel calibrated to throughput and material availability.
- Experience working with computational or AI/ML colleagues on closed-loop assay programmes.
- Background at an AI-native biotech or foundation-model company.
Eligibility Criteria
No explicit eligibility statement; the job may be open to various applicants but lacks clarity.
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