AI for biology has a data problem, not a compute problem: biological foundation models can predict but cannot experiment, and the large-scale, high-quality data they need does not exist.
We are building the lab that 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 Mostafa ElSayed, co-founder and CEO; Oli Hoy, who leads the lab buildout and operations; Alexey Morgunov, who leads science and intelligence software; and Anna Huyghues-Despointes, who leads partnerships and go-to-market.
The roleYou will run the day-to-day operations of our lab in London, and be the person the scientists come to when something is missing, broken, late or lost.
The day-one work is what you would expect: consumables and stock, goods in and cold chain, equipment servicing and calibration, suppliers and purchasing, waste streams and safety records.
Three parts go beyond a standard lab operations job.
The first is provenance.
Every experiment we sell carries a record of what went into it, down to the lot of the consumable used and the conditions material was held in.
If a lot number is not captured, the data point is worth less, and that record runs through you.
The second is that the systems are not written yet.
Inventory, goods in, asset registers and ordering exist in no form that will survive the next year.
You define what the lab has to capture and work with the science and software teams to make sure it does.
The third is that sample handling and stock control here get automated, and you help specify the system that does it.
Every sample, plate, reagent and consumable lot carries a barcode, scanned at each point it is handled, so the record is created by the work rather than reconstructed afterwards.
The same scan draws stock down, which turns reordering from a weekly count into something the system flags before anyone is short.