Substrate is building the protein production cascade that turns a sequence into characterised, quality-controlled protein at scale, and you will build it from the first manual run. This is the work upstream of every protein data point Substrate generates: DNA design, molecular biology, protein expression, purification, and quality control, 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 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 centre of the protein work.
You will build the protein production cascade that sits upstream of molecular characterisation and functional assays: DNA design, molecular biology, protein expression, purification, and quality control through analytical characterisation. The cascade will eventually run autonomously on Substrate’s automation platform. In the first phase, you develop each step by hand, running protocols manually, setting reproducibility and quality thresholds, and proving each phase works 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. You will work directly with the automation engineering and software teams on the boundary between scientific protocols and autonomous execution, at Scientist or Senior Scientist level, alongside Principal Scientists at the bench, and you will grow into protocol authorship over the first year.
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 protein production workflows as the day-one assay menu locks.
Start building DNA design and production (linear DNA), protein expression (cell-free), and protein purification and QC at high throughput. 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.
Have a linear DNA, cell-free expression, purification, and QC workflow in a semi- automated state, running at the scale of hundreds to thousands per week and ready for full automation.
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.
Start building DNA design and production (plasmid DNA), protein expression (mammalian), and protein purification and QC at high throughput, again by hand on equipment that will move onto the platform.
Build reproducibility, precision, and acceptance thresholds into the workflow.
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, running, andvalidating at the bench.
You are comfortable in the details. You have hands-on experience with high throughput, plate-based methods across molecular biology, protein expression, purification, and QC.
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.
This role is open at Scientist and Senior Scientist level. The work is hands-on bench science at both, with collaboration into automation and software; the difference is depth of ownership, design authority, and how much of the team you help build. We do not hire people into boxes, and the early team stretches beyond the strict edges of any role.
Comfortable handling DNA design and linear and plasmid DNA production at high throughput, expressing proteins, and purifying them with QC steps for validation, executing protocols at the bench through manual, semi-automated, and instrumented phases.
Core experience within biologics.
A track record of working alongside non-scientist colleagues (automation, software, computational) on a shared workflow.
A PhD in protein biochemistry, molecular or structural biology, biophysics, analytical characterisation, or biologics developability, with two or more years of relevant hands-on experience; or a relevant science MSc with five or more years of equivalent bench experience in protein science.
Independent, hands-on competence across protein science: molecular biology (DNA production with Gibson and Golden Gate assembly at transfection-grade quality), protein expression (cell-free, IVTT, E. coli and mammalian), and protein purification (automated magnetic-bead workflows), with exposure to at least one of these at high throughput (24, 96, and 384-well plate formats). Protein characterisation across at least two core biophysical methods (for example CE-SDS, DLS, nanoDSF/DSF, analytical SEC, HIC, or LC-MS intact mass analysis).
Confident data analysis, including telling instrument artefacts apart from genuine molecule behaviour.
Fluency with structured experimental data capture, and proficiency with a LIMS, ELN, or analogous data infrastructure.
Ready to grow into protocol authorship and SOP ownership over the first twelve months.
Direct experience moving biophysical or analytical assays from manual workflows onto lab automation platforms (at least a handoff into an automated platform).
High throughput experience in at least one core biophysical platform (SPR or BLI for binding kinetics), plus one or more orthogonal characterisation or developability assays (for example nanoDSF/DSF, DLS, analytical SEC, HIC, AC-SINS, cIEF, or CE-SDS).
Experience developing novel high throughput technologies in DNA generation, cell expression (cell-free, mammalian), or bead-based purification.
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.
Experience working with computational or AI/ML colleagues on closed-loop assay programmes.
Background at an AI-native biotech or foundation-model company.
Most protein science roles like this inherit an established workflow and optimise 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 20 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. Mostafa ElSayed is co-founder and CEO, and founder and CEO of Automata, whose automation hardware runs the lab. 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.
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.
