Member of Scientific Staff, Cell Line Development

LOCATION: King’s Cross, London · PATTERN: Lab-based, full-time

We’re a stealth startup in AI and bio, building infrastructure for data generation. The team is small and elite, the problems are hard, and the foundations are being laid right now. You’d help write them from the first line, with real ownership and a direct line to the founders.

This role builds our cell lines from the start. You would engineer, validate and bank the human cell lines our functional genomics screens run on, in a lab where the methods you set become the standard.

AI for biology has a data problem. The data that matters most doesn’t exist yet, so we’re building the infrastructure to produce it, with quality and traceability built in from day one.

We’re in stealth and heads-down on execution. We’ll share more about what we’re building once you’ve spoken with the team.

You will build and look after the cell models behind our screens. That means human iPSCs and other cell types that are hard to culture, taken from a vial on arrival to a validated, documented bank that a screening campaign can draw on.

Most of the work is engineering. You will generate CRISPR-edited lines, including knockouts, knock-ins and endogenous tags, isogenic disease and corrected pairs, and inducible systems. You will isolate clones and validate them by genotyping, sequencing, karyotype and pluripotency QC, and off-target checks. We build our engineered lines in-house rather than buying them in, so this work starts at the beginning of the line, not partway through.

You will differentiate iPSCs into neurons and other lineages and characterise them by immunostaining and imaging, then hand them on to the screening, imaging and computational scientists you work alongside. You will build and manage the cell banks, with records good enough that anyone can trace a vial back through every passage, edit and QC result.

As protocols settle, you will make them more consistent and move them into plate-based formats ready for automation. This is a hands-on bench role in a small team, and the protocols you write here are the ones the lab will run.

FIRST 30 DAYS

◆ Run our core iPSC and cell culture workflows independently and to a consistent standard.

◆ Learn the cell-line register, the QC gates a line passes through, and how its history is recorded.

DAYS 30 TO 60

◆ Take a first engineered line from guide design through editing and into clonal isolation.

◆ Write or revise at least one cell maintenance protocol, including how failures are captured.

DAYS 60 TO 90

◆ Bring that first line through validation, with banking under way.

◆ Improve one protocol for consistency or move it into a plate-based format.

You are a careful, hands-on cell biologist who cares about cells behaving the same way every time. You have spent long enough with iPSCs or similarly demanding cells to know what a bad week in culture looks like and how to stop it happening again. You keep records that someone else can follow without asking you.

You are happy at the bench most days and you like seeing your work picked up by the people downstream of you. You say early when something is not working, and you are comfortable with a plan that changes as we learn.

We do not hire people into boxes. The team is small, and early hires here end up owning work beyond the job they joined for. If you want to learn how screening, imaging and automation fit around your cells, there is room to do it.

MUST HAVE

◆ A PhD in cell biology, stem cell biology, neuroscience or a related field, or an MSc and two to three years of relevant lab experience.

◆ Strong hands-on experience culturing iPSCs or other demanding cell types, such as primary cells, neurons or organoids.

◆ Practical CRISPR genome editing and clonal line generation, with the molecular biology behind it (cloning, PCR, Sanger and NGS genotyping).

◆ Excellent aseptic technique and careful, traceable record-keeping.

NICE TO HAVE

◆ Forward programming of iPSCs into neurons or other cell types by transcription factor induction, for example NGN2.

◆ Lentiviral production and transduction.

◆ Fluorescence or high-content imaging, or flow cytometry for single-cell sorting.

◆ Work with lab automation, such as liquid handlers or plate-based workflows.

Most cell line roles sit inside a research group, making lines for one project and one set of hands. This one sits inside a lab built around data quality, and that changes the job in two ways.

The history of every line is part of the record. Source, passage, edits and QC results are captured as structured data, because results are only as trustworthy as the cells behind them. And the protocols you write become the starting point for automated workflows, so they have to be precise enough for a liquid handler to follow.

That means more documentation and more standardisation than most academic labs ask for. Some people