Molecular Characterisation Scientist, Biologics

Job description

The opportunity Substrate is building the molecular characterisation cascade that turns purified protein into trustworthy, quality-controlled, biophysically characterised data at scale, and you will build it from the first manual run. This is the work downstream of protein production and upstream of functional assays: quality control, binding, stability, and developability, developed by hand and engineered to move onto automation. The data this cascade produces is the product. Substrate is the critical infrastructure layer between AI and biology, and biological foundation models can predict but cannot experiment; the high-quality, large-scale data they need does not exist yet. Your work at the bench is what brings it into existence. About Substrate 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. The role You will build the molecular characterisation cascade that sits downstream of protein production and upstream of functional assays: the biophysical, analytical, and developability assays that decide what each molecule is and whether it holds up. The cascade will eventually run autonomously on Substrate’s automation platform. In the first phase, you develop each assay by hand, running protocols manually, setting reproducibility and quality thresholds, and proving each assay out before it moves onto instrumentation. As the automation platform comes online, the work shifts toward instrumented execution, equivalence validation, and the engineering judgement calls that decide which manual steps get automated and which stay in human hands. Every level works directly with the automation engineering and software teams on the boundary between scientif…