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.
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 they cannot run experiments, and the high-quality, large-scale data they need does not exist. Substrate generates it, with quality and provenance built in.
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 scientific protocols and autonomous execution.
Principal Scientists own a slice of the assay menu end to end: scoping, designing the manual protocol, validating it to acceptance thresholds, authoring the SOPs that translate into automation design, and seeing it through to automated execution.
Scientists work alongside Principal Scientists at the bench, executing the experiments, contributing to validation work, and growing into protocol authorship over the first year.
Lab Technicians are the hands-on execution layer: following established SOPs, preparing reagents and consumables, maintaining equipment, and running routine steps of validated protocols so that Scientists and Principal Scientists can focus on design and troubleshooting.
Land in the lab. Set up your bench at the London site and start manual assay development alongside a senior member of the protein sciences vertical.
Get hands on the first priority characterisation assays as the day-one menu locks. Start building QC (purity, concentration, oligomeric state), binding (SPR or BLI), and stability (nanoDSF, DLS) readouts. Run them by hand on equipment that will eventually move onto the automation platform, capturing the data-structure and metadata decisions that translate into automation design.
Build reproducibility, precision, and acceptance thresholds into the workflow.
Contribute to the day-one menu decisions and begin authoring SOPs for your slice of the assay portfolio. Help interview the scientists and lab technicians joining alongside you.
Develop and validate a QC (purity, concentration, oligomeric state), binding (SPR or BLI), and stability (nanoDSF, DLS) workflow, first manually and then in a semi-automated state, running at a scale of hundreds to a thousand samples per week and ready for full automation.
Begin development of a developability package (HIC, heparin binding, AC-SINS, and similar), with assay design guided primarily by downstream automation compatibility.
Co-design protocols with the software and automation engineering teams so the manual versions you validate are automation-ready by design. Decide which manual judgement calls have to be engineered out before they reach the platform.
Contribute to co-design conversations with the first commercial customers, including the foundation-model partners coming online from 2027.
Workcells arrive in the lab. Move the validated assays onto them, running with instrumentation and human intervention in the loop. Validate equivalence against your manual baselines and triage failures.
Open the assay menu to customers through manual and semi-automated services. Run real experiments for real customers.
Help bring on the next scientists and lab technicians as the vertical grows.
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.
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.
Hands-on experience with biophysical and/or analytical instrumentation for protein characterisation.
Comfortable executing assays in a high-throughput format through manual, semi-automated, and instrumented phases.
A track record of working alongside non-scientist colleagues (automation, software, computational) on a shared workflow.
Relevant hands-on lab experience, including apprenticeships or technician roles, is the primary consideration.
Comfortable following written SOPs precisely and flagging deviations. You do not need to design experiments, but you do need to execute them reliably and communicate clearly when something looks off.
Hands-on familiarity with basic wet-lab technique: accurate pipetting, buffer and reagent preparation, sample handling, plate setup, and instrument use. Exposure to plate readers, chromatography systems (HPLC), biophysical instruments (nanoDSF, DLS, SPR/BLI), or semi-automated workflows (plate-based liquid handlers) is helpful but not required.
Comfortable in a fast-paced, early-stage environment where protocols are still being written. You are methodical, safety-conscious, and do not take shortcuts.
Aware of structured experimental data capture, and able to use a LIMS, ELN, or analogous infrastructure.
A PhD in protein biophysics, analytical characterisation, or biologics developability, with two or more years of relevant hands-on experience; or an MSc with five years of experience in the same area.
Independent, hands-on competence on at least one biophysical binding platform (SPR or BLI), plus one or more orthogonal characterisation or developability assays (for example nanoDSF/DSF, DLS, analytical SEC, HIC, AC-SINS, cIEF, or PAIA).
Some exposure to one or more of these areas at high throughput (96 and 384-well plate formats).
Confident data analysis: kinetics fitting and interpretation, and telling instrument artefacts apart from genuine molecule behaviour.
Fluency with structured experimental data capture, and proficiency with a LIMS, ELN, or analogous infrastructure.
Ready to grow into protocol authorship and SOP ownership over the first twelve months.
A PhD in protein biochemistry, biophysics, analytical characterisation, or biologics developability, with five or more years of relevant hands-on experience (at least two in industry); or a relevant science MSc with eight years of equivalent bench experience in protein science (at least two in industry).
Independent, extensive experience with biologics characterisation workflows across at least three core areas, such as full SPR/BLI kinetics, epitope binning, immunoassay (ELISA)-based assays, thermal and colloidal stability characterisation (nanoDSF, DLS, SLS, analytical SEC), and developability assessment (HIC, heparin LC, AC-SINS, forced degradation).
Experience in two or more of these areas at high throughput (96 and 384-well plate formats), establishing and optimising workflows, SOPs, and validation.
Proficiency with structured experimental data capture using a LIMS, ELN, or analogous infrastructure.
Method development and validation experience: defining acceptance criteria, references, and controls that other scientists have run successfully.
SOP and protocol authorship that others have executed, and experience supervising at least one junior scientist or technician.
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.
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.
This is an in-person, lab-based role at Triton Street in London. The manual development phase is hands-on, and its cadence is heaviest early, easing as protocols move onto instrumentation.
The UK package is 30 days of annual leave plus public holidays, a pension with a 10% employer contribution, and top-tier Bupa private health cover, with more added as the team grows. The founding team is distributed across London, New York, and Europe and travels; we run on a weekly written update and two weekly all-hands with an open, non-anonymous AMA, and the day-to-day happens in Slack.
Substrate is four co-founders and a fast-growing team. Oli Hoy, co-founder, leads infrastructure, software, and operations, and owns data provenance. Alexey Morgunov, co-founder, owns the science and intelligence work and the scientific roadmap, and owns scientific quality. Anna Huyghues-Despointes, co-founder, leads partnerships, commercial strategy, and go-to-market. Mostafa ElSayed is co-founder, and founder and CEO of Automata, whose automation hardware runs the lab.
You will join the protein sciences vertical, working day to day alongside a senior member of the vertical and the automation engineering and software teams who turn the manual cascade into an automated one.
Our process has four stages: a short screening conversation on logistics and the role; a behavioural and cultural-fit conversation, which we run before the technical stage; a technical assessment or work sample with the team; and references.
If you are not sure whether you are a fit, apply anyway. The shape of the work is unusual, and the strongest people in it do not always arrive with the obvious background. Substrate is an equal opportunity employer. We make hiring decisions on merit, scope-fit, and the strength of the working relationship we expect to build with each hire. Applications welcome from candidates of any background.
