Adaptyv is building an automated lab that lets AI agents run biology experiments.
We're entering the era of agentic science where AI models can now design novel proteins, propose hypotheses, and iterate on experimental results. But they can't run the experiments themselves - that's still a manual, months-long process. We're building the infrastructure that gives AI agents access to the physical world.
We are one of the fastest growing biotech companies, trusted by leading biopharmas, frontier AI labs, and the techbio companies pushing the field forward. This is a rare chance to help advance some of the most important work happening in biotech today.
Our automated lab is powered by a deep software + hardware stack: lab instruments worth millions of USD reverse-engineered into API-controllable hardware, dozens of devices orchestrated through complex workflows, full observability on everything that happens in the lab, processing pipelines for messy physical-world data, and AI systems that troubleshoot production results and accelerate assay development.
We’re growing rapidly and are hiring for talented people to scale and support the massive demand for AI-driven wet lab experimentation.
We are building the most automated lab in the world, and that does not stop at the bench. A company that AI agents can run experiments in has to automate everything around the experiment too — procurement, hiring, admin, and finance. Finance is one of the most automatable functions in any company and almost nobody has actually done it. We intend to.
That is the job: build an AI-powered finance stack. Our ledger, billing, cards and payroll all expose APIs, and we already close the books against them programmatically rather than by hand. You would take that much further, to where the model, the close, the cash view and the margin analysis largely run themselves, and your week goes on the decisions instead of the reconciliation.
You would be our first finance hire, and it is hands-on. You will build the forecast, run the close across two entities, own cash, and be the person who can say what a protein actually costs us to make.
Own the forecast. Both directions: bottom-up from lab capacity, throughput and price per protein, and top-down P&L, burn and runway.
Own gross margin. What does an experiment actually cost us — DNA, reagents, consumables, instrument time, labour? Which products and which customers lose us money?
Own pricing, with commercial. What we charge, why, and where the floor is. Pricing at a lab company is a finance question wearing a sales hat.
Own the close. Group consolidation and intercompany management. Make it fast, make it correct, make it boring.
Own cash. Bank and card accounts across Switzerland and the US, AP runs, AR collection, payment timing. Know where the money is and where it is going before anyone asks.
Keep us compliant. Tax, VAT, audit, payroll, and the external firms we work with. You are not a lawyer or an accountant, but you know when to call one.
Investor reporting, fundraising support, and treasury management.
Equipment leasing and asset management. The lab runs on millions of francs of instruments. Lease or buy, how we finance it, what it does to the balance sheet, and keeping the asset register honest as we expand.
Automate all of the above.
You have built a finance function, not just worked in one. Several years in finance at a startup that actually scaled, and ideally you were early enough to have set things up rather than inherited them.
You can build a real model. Not a template. A model that ties operational reality — throughput, yield, capacity, headcount — to cash, and that survives someone pulling on it.
You write code, or you are visibly about to. SQL at minimum, Python ideally. This is the hard requirement most finance candidates will fail, and we mean it. We run on Claude Code across the company, and every system finance touches has an API behind it. The best version of this person automates their own function, writes their own tooling, and finds leverage the rest of us have not thought of. If your instinct is that finance is a spreadsheet-and-portal job, we will frustrate each other.
You understand the cost structure of a physical business. Reagents, instruments, consumables, capex versus COGS, yield loss. Lab, hardware, or manufacturing backgrounds all translate. Pure SaaS finance does not, on its own.
Swiss and European finance experience. VAT, social charges, employment law. A US entity alongside a Swiss one is a plus.
Ambitious, and in it for the company. We want someone who intends to be running finance at a much larger Adaptyv in three years, and who cares about what we are building rather than the title on the door.
Trustworthy with everything. Salaries, revenue, investor communications, board material. All of it crosses your desk.
We are a small company and nobody here has clean edges around their job. You will end up in the lab, in a room with a customer, and every so often doing something that is nobody's job simply because it needs doing.
The centre of gravity is the numbers: the model, the close, cash, margin, pricing, and the board pack. That is the part that is yours and the part we will judge the role on. Procurement is a separate hire and the two of you will be joined at the hip on cost, but the sourcing and the vendor relationships are not yours to run.
One thing it is not: a bookkeeping seat. The mechanical layer is handled, and if that is the job you want, this one will bore you.
Most first-finance-hires at a startup spend three years cleaning up a SaaS P&L. Here, the finance question and the product question are the same question: an automated lab is a machine for converting money into experimental data, and how efficiently it does that is literally the business. You will be building the model of that machine, at a company that is about to expand it several-fold, while it is being used by the best pharma companies and the best AI labs in the world. And you will do it with a founding team that will hand you the API keys and get out of your way.
Application deadline
We are reviewing applicants on a rolling basis.
