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About the Role

Substrate is building the molecular characterisation cascade that turns purified protein into trustworthy, quality-controlled, biophysically characterised data at scale. This role is critical infrastructure between AI and biology, generating high-quality, large-scale data that biological foundation models need but do not yet have.

Responsibilities

• Build the molecular characterisation cascade downstream of protein production and upstream of functional assays.
• Develop each assay by hand, running protocols manually, setting reproducibility and quality thresholds, and proving each assay out before it moves onto instrumentation.
• Shift toward instrumented execution, equivalence validation, and engineering judgement calls as the automation platform comes online.
• Work directly with automation engineering and software teams on the boundary between scientific protocols and autonomous execution.
• Scope, design manual protocols, validate them to acceptance thresholds, and author SOPs that translate into automation design.
• Execute experiments and contribute to validation work.
• Follow established SOPs, prepare reagents and consumables, maintain equipment, and run routine steps of validated protocols.
• Set up your bench at the London site and start manual assay development.
• Build QC (purity, concentration, oligomeric state), binding (SPR or BLI), and stability (nanoDSF, DLS) readouts.
• Run assays by hand on equipment that will eventually move onto the automation platform, capturing data-structure and metadata decisions.
• Build reproducibility, precision, and acceptance thresholds into the workflow.
• Contribute to day-one menu decisions and begin authoring SOPs for your slice of the assay portfolio.
• Help interview scientists and lab technicians joining the team.
• Develop and validate a QC, binding, and stability workflow, first manually and then in a semi-automated state, running at scale.
• Begin development of a developability package, with assay design guided by downstream automation compatibility.
• Co-design protocols with software and automation engineering teams so manual versions 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 commercial customers.
• Move validated assays onto workcells, running with instrumentation and human intervention.
• Validate equivalence against 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.

Requirements

• Protein scientist excited about designing, validating, and running biophysical and developability assays at the bench.
• Comfortable in the details.
• Hands-on experience collecting and analysing binding kinetics and affinity data, and assessing the developability of biologics.
• Attracted to assays designed for autonomous execution from day one.
• Write good SOPs and hold yourself and colleagues to clear reproducibility thresholds.
• Pragmatic about being hands-on in the early phase.
• Enjoy working at the boundary with non-biologist colleagues.
• 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.
• Track record of working alongside non-scientist colleagues on a shared workflow.
• Relevant hands-on lab experience, including apprenticeships or technician roles.
• Comfortable following written SOPs precisely and flagging deviations.
• 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.
• Methodical, safety-conscious, and do not take shortcuts.
• Aware of structured experimental data capture, and able to use a LIMS, ELN, or analogous infrastructure.
• 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.
• 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.
• Experience developing a tiered developability or characterisation cascade, mapping properties 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.

Skills

• Protein characterisation
• Assay development
• Biophysical assays
• Analytical assays
• Developability assays
• Quality control (purity, concentration, oligomeric state)
• Binding assays (SPR or BLI)
• Stability assays (nanoDSF, DLS)
• Reproducibility
• Quality thresholds
• SOP authoring
• Automation
• Data analysis
• Binding kinetics
• Affinity data
• Developability assessment
• High-throughput assay execution
• Wet-lab technique
• Pipetting
• Buffer and reagent preparation
• Sample handling
• Plate setup
• Instrument use
• Plate readers
• Chromatography systems (HPLC)
• Semi-automated workflows (plate-based liquid handlers)
• Structured experimental data capture
• LIMS
• ELN
• Kinetics fitting
• Method development
• Method validation
• Epitope binning
• Immunoassay (ELISA)-based assays
• Thermal and colloidal stability characterisation (nanoDSF, DLS, SLS, analytical SEC)
• Developability assessment (HIC, heparin LC, AC-SINS, forced degradation)
• AI/ML

Location

• London

Work Type

• In-person
• Lab-based

Experience Level

• Principal Scientist
• Scientist
• Lab Technician

Education Level

• PhD
• MSc

Benefits

• 30 days of annual leave plus public holidays
• Pension with a 10% employer contribution
• Top-tier Bupa private health cover

About the Company

• 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.

Equal Opportunity

• 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.