About Biomatter

Biomatter designs enzymes that never existed in nature - proteins that were, until recently, impossible to create. They're already at work in health, food, agriculture, and sustainable manufacturing: companies like BASF, Thermo Fisher, and Kirin build their products on enzymes we designed.

Behind this is our platform, which combines state-of-the-art AI with our own wet laboratory. Algorithms design new proteins, the lab builds and tests them, and every experiment makes the next design better. That's why our team spans software engineers, AI researchers, and scientists at the bench.

The role

Today our lab runs on islands of automation. Some stages are already partly automated on liquid handlers and robotic systems, others are still done entirely by hand, and in between plates, samples and files move from one step to the next manually. It works, but it doesn't scale - every manual handoff costs time, adds room for error and takes scientists away from science.

We're creating our first dedicated lab automation role to connect those islands. The job is to build one continuous path from a sequence designed in silico to the data measured for it in the lab, so that every designed sequence is tracked, produced and measured the same way, and its data flows straight back into the next design round. How quickly and reliably that loop turns decides how much the platform learns per round - which makes this role a direct contributor to the speed of the whole company.

This is a hands-on engineering role between the bench and the code, and it calls for resourcefulness. We believe in pragmatic engineering: the best solution is not always the biggest or the most expensive one. Sometimes the right answer is a better method on equipment we already have, a Python script, a 3D-printed adapter or a second-hand instrument - and sometimes it really is a new purchase. We're looking for someone who can tell the difference, and who is as comfortable writing a liquid-handler method or a script as running the manual version first to see where it breaks.

What you'll own

From designed sequence to measured data

• Map the full path from designed sequence to measured data: which steps are automated, which are manual, and where samples and data change hands.

• Connect the islands of automation into one continuous workflow, removing manual handoffs where it pays off.

• Keep every designed sequence traceable end to end - from the design file through DNA, plates, wells and samples to the measured result.

• Build the links between our computational platform and the lab: designs in as worklists and plate maps, measured data out in a form that feeds straight into the next design round.

• Work with the computational team to shorten the time it takes to go from a new set of designs to their data.

Automated methods and workflows

• Automate manual steps where the gain is worth it, starting with the ones that cost the most time or cause the most errors.

• Translate manual protocols - protein expression, purification and plate-based enzyme assays - into robust methods on our liquid handlers and robotic systems.

• Own methods end to end: deck layouts, labware definitions, liquid classes, tip strategies and error handling.

• Scale workflows from a single plate to hundreds of plates per run without losing accuracy or reproducibility.

• Validate every new or changed method against its manual equivalent before it goes into routine use, and keep methods under version control.

Equipment and uptime

• Be the technical owner of the lab's automation equipment: liquid handlers, automated purification systems, plate readers, dispensers and their peripherals.

• Troubleshoot hardware, software and method failures, and find the root cause so the same failure doesn't happen twice.

• Run preventive maintenance and calibration together with lab operations, and coordinate vendor engineers when an instrument needs a service visit.

• Treat downtime on critical instruments as the priority it is.

Making the most of what we have

• Get the most out of the equipment we already own before reaching for new purchases.

• Make sensible build vs. buy decisions - modify, script, 3D print or buy second-hand where that does the job, and invest in new equipment where it clearly pays off.

• Help specify, evaluate and select automation equipment for the high-throughput lab in our new ~700 m² facility, and support its installation and setup.

• Use AI-assisted coding and other modern tools wherever they make you faster.

People and collaboration

• Work closely with scientists, biotechnologists and analytical chemists to understand what each workflow has to deliver.

• Write and maintain SOPs for automated methods and equipment.

• Train colleagues to run automated workflows independently, so the lab doesn't depend on you to press start.

What we need

• A degree in biotechnology, bioengineering, biochemistry, chemistry, engineering, computer science or a related field - or equivalent hands-on experience.

• At least 2 years of hands-on experience developing and running methods on automated liquid handlers (Hamilton, Tecan, Beckman, Opentrons or similar) in a biotech, pharma or academic lab.

• Experience connecting the steps of a workflow - instruments, sample tracking and data - not only programming a single instrument.

• Enough wet-lab experience to understand what a protocol is trying to achieve and why each step matters - ideally with plate-based assays and protein work.

• Working proficiency in Python or a similar language for scripting, instrument control and moving data between systems.

• Resourcefulness and flexibility. You find a workable solution with the equipment, tools and budget at hand, and you're happy to switch between building, fixing and running things as the day requires.

• A troubleshooter's mindset. You want to know why a run failed, whether the cause is mechanical, software or chemistry.

• Discipline about documentation, validation and version control - a method that only you understand is not finished.

• Excellent written and oral English communication skills.

Nice to have

• Experience automating protein expression, purification or enzyme assay workflows.

• Familiarity with sequence-level data - DNA and protein sequences, variant libraries, plate maps - and how it moves between design tools and the lab.

• Experience with open-source or low-cost automation (e.g. Opentrons, PyLabRobot) as well as commercial scheduling software (e.g. Green Button Go, Cellario).

• Experience integrating automation with LIMS, ELN or other data systems.

• Hardware skills: CAD, 3D printing, basic electronics.

• Experience in high-throughput screening, or in setting up a new automated lab.