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.
Protein science is the first scientific vertical we are bringing online. The vertical covers expression and purification, biophysical characterisation, cell-based functional assays, and developability assays. Manual development of the day‑1 assay menu is starting now, and full autonomous execution on workcells is targeted for mid‑2027. You and the Protein Scientist will be the bench scientists who develop, validate, and operate the assays through that transition: running protocols by hand, setting reproducibility and quality thresholds, working alongside automation engineers as the assays move onto instrumentation, and validating equivalence at every step. The Head of Protein Science you will work under is being hired alongside you.
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 be at the bench. The work is to develop, validate, and operate the assays that will eventually run autonomously on Substrate’s workcells. In the first phase, that means manual assay development: running protocols by hand, setting reproducibility and quality thresholds, and proving each assay out before it moves onto instrumentation. As the workcells arrive, 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 own a slice of the assay menu end to end: scoping it with the Head, designing the manual protocol, validating it to acceptance thresholds, authoring the SOPs that translate to workcell design, and seeing it through to automated execution. Scientists will work alongside you at the bench, executing the experiments, contributing to validation work, and growing into protocol authorship over the first year. You will work directly with our automation engineering and software teams on the boundary between scientific protocols and autonomous execution.
What is unusual about the work is that 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 – output structure, metadata, provenance, consistency across runs – is treated as a first‑class scientific constraint, because that data feeds directly into foundation model training pipelines for our customers. Your decisions at the bench affect what the orchestrator has to do and what data flows back to model partners.
What you will do in your first twelve months
PHASE 0: NOW TO AUG 2026
- Land in the lab. Set up your bench at our King’s Cross site and start manual assay development alongside the Head of Protein Science.
- Get hands on the first priority assays as the day‑1 menu locks. Run protocols by hand, capture the data structure and metadata decisions that will translate to workcell design.
- Contribute to the day‑1 menu decisions and begin authoring SOPs for your slice of the assay portfolio. Help interview the Scientists and Lab Technicians joining alongside.
PHASE 1: SEP TO DEC 2026
- Develop and validate the first protein assays manually. Set the reproducibility and quality thresholds that will serve as acceptance criteria for the moves to instrumented and to fully autonomous execution.
- Co‑design protocols with the software and automation engineering teams so that the manual versions you validate are automation‑ready by design. Decide which manual judgement calls have to be engineered out before they hit a workcell.
- Contribute to co‑design conversations with the first commercial customers, including the foundation model partners coming online from 2027.
PHASE 2: JAN TO MAR 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 via manual and semi‑automated services. Run real experiments for real customers.
- Help bring on the next Scientists and Lab Technicians as the vertical grows.
Who you are
You are a protein scientist who is excited about doing the actual work – designing, running, validating, and iterating on assays at the bench. You are comfortable in the detail. You have done at least one modality end to end. The shape of the problem is what attracts you: assays that have to be designed for autonomous execution from day one, in a business where the data the lab produces is itself part of the product.
You write good SOPs. You hold yourself and your colleagues to clear reproducibility thresholds. You are pragmatic about being hands‑on in the early phase, with cadence that will be heavy in the manual development phase and ease 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 with them.
We are hiring across both Scientist and Principal Scientist levels. The shape of the work is similar at both tiers: hands‑on bench science, with collaboration into automation and software. The difference is depth of ownership and team responsibility.
MUST HAVE
- Hands‑on experience in protein expression and purification, with at least one biophysical or functional assay run independently.
- Comfortable executing protocols at the bench through manual, semi‑automated, and instrumented phases.
- Track record of working alongside non‑scientist colleagues (automation, software, computational) on a shared workflow.
- Seven or more years of experience in protein science, with at least one assay class owned end to end: scoping, design, validation, SOP authorship, and handoff to automation or production.
- SOP and protocol authorship that other scientists have run successfully. Has supervised at least one junior scientist or technician.
NICE TO HAVE
- Direct experience moving protein assays from manual workflows onto lab automation platforms.
- Familiarity with structured experimental data capture, LIMS, ELN, or analogous data infrastructure.
- Experience working with computational or AI/ML colleagues on closed‑loop assay programmes.
- Background at an AI‑native biotech or foundation model company.
Why this is unusual
Most protein science bench roles in industry sit either inside a pharma R&D group (slow iteration, internal customers only) or inside a CRO (external customers, faster iteration, but optimised for service throughput rather than scientific decisions about assay design). This is neither. You will be developing assays that have to be automation‑ready from the first manual experiment, working alongside foundation model labs on closed‑loop programmes that do not have a precedent in either pharma or CRO settings, and contributing to the proprietary dataset programme that turns the lab itself into a commercial asset.
It is also a protein science role with significant software and AI surface area. The way you design and run assays affects what the orchestrator has to do, what data flows back to model partners, and which manual judgements get re‑engineered out of the workflow. Some scientists find that energising; some find it outside their lane. Worth knowing in advance which one you are.
Compensation and equity
We pay competitively against the London market for protein scientists at venture‑backed companies, with bands calibrated to seniority and to the specific scope of the role. We will discuss numbers with serious candidates after first conversations.
Options on the standard four‑year vest with a one‑year cliff. The specific quantum varies by seniority and we will discuss it with serious candidates.
How we work
Working pattern is in‑person and lab‑based. You will be at our King’s Cross site most days; that’s where the experiments are. Some flexibility for desk‑based work – protocol writing, data analysis, reading – but the cadence is set by the science.
30 days annual leave. A learning budget you can use for conferences, courses, books, and time. The founding team operates on a weekly cadence with a Monday planning meeting and a Friday close, and a quarterly off‑site. We are direct with each other, we write things down, and we expect to be challenged.
The team you will join
You will initially report to Alexey Morgunov, our science and AI focused co‑founder. The Head of Protein Science, who is being hired alongside this team, will be your interim manager until they are in seat. You will work most closely with the other protein scientists at the bench, with the Lab Technicians who execute alongside you, with the functional genomics team across the other vertical, and with the software and automation engineering teams on the boundary between scientific protocols and autonomous execution.
Equal Opportunity Statement
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.
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