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.

About the Role

You are here to scale up data generation. Design teams can hand us 10⁶ sequences, and our automated infrastructure measures on the order of 10³ of them individually. Library construction and display is what closes that gap.

We want someone who has set these systems up before and can do it again here, with their own hands. Phage, ribosome, mRNA, yeast, cell-free: which one fits depends on the library and the target, and part of the job is making that call and then building it rather than writing a recommendation.

This is a build role, not a service role. For your first months you are making libraries, running selections, working out why a round collapsed, and turning what works into something the automation team can run without you.

What You'll Do

• Build the libraries. Oligo pools, combinatorial assembly, barcoding. More campaigns fail here than at selection.

• Run selections end to end, including the call on whether a round enriched real binders or just fast growers.

• Pick the platform per campaign and stand it up. Based on the library and the target, not on what you happened to use last.

• Own the sequencing analysis. You don't need to be a bioinformatician, but you can't be waiting on one.

• Hand off to automation. A protocol that only works when you personally run it isn't finished.

• Close the loop. Selected designs get expressed and measured on BLI and SPR by the team next door. That tells you whether the selection worked.

What We're Looking For

• MSc or PhD in a relevant field, plus 3+ years running display and selection yourself.

• You have set up a display platform, not just used one. This is the core requirement. Built the library, got the selection working, and produced binders that held up in an independent assay. Managing outsourced CRO campaigns is not the same thing.

• Depth in at least one display format and working literacy across the rest. We are not fixed on which one.

• High-diversity cloning, and an understanding of where bias enters a pool and what it costs you later.

• NGS as a routine tool, and FACS if you have it.

• Enough Python or R to analyze your own data without joining a queue.

• You use AI tools seriously. It's 2026 and we run on Claude Code across the company. You also need the judgment to check what comes back.

• A self-starter. Nobody is going to hand you a prioritized queue. You decide what to build, build it, and tell us what you learned.

• Startup speed, not academic pace. Campaigns run against customer deadlines. A working platform with known limits beats an elegant one next year.

• You want your protocols automated rather than manual forever.

If your instinct is that a campaign is finished once you have ten good clones, we will frustrate each other. We want data, not a hit list.

Why This Role Is Interesting

Most display scientists spend a career panning one target class, and the output is a hit list. Here you would run campaigns across many targets and many design methods, much of it on AI-designed proteins nobody has characterized before.

Expression and characterization already run at scale on our automated infrastructure, so you build the selection layer and not everything underneath it.

Details

• Location: Lausanne, Switzerland, on-site. This is a lab role.

• Type: Full time.

• Start date: As soon as you can.

Application deadline

We are reviewing applicants on a rolling basis.