Bioengineer, Display & Library Engineering

ppbAdaptyv is building an automated lab that lets AI agents run biology experiments. /b /p pWe'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. /p pWe 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. /p pOur 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. /p pWe're growing rapidly and are hiring for talented people to scale and support the massive demand for AI-driven wet lab experimentation. /p h3About the Role /h3 pYou are here to scale up data generation. Design teams can hand us 10sup6 /sup sequences, and our automated infrastructure measures on the order of 10sup3 /sup of them individually. Library construction and display is what closes that gap. /p pWe 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. /p pThis 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. /p h3What You'll Do /h3 ul lipbBuild the libraries. /b Oligo pools, combinatorial assembly, barcoding. More campaigns fail here than at selection. /p /li lipbRun selections end to end /b, including the call on whether a round enriched real binders or just fast growers. /p /li lipbPick the platform per campaign and stand it up. /b Based on the library and the target, not on what you happened to use last. /p /li lipbOwn the sequencing analysis. /b You don't need to be a bioinformatician, but you can't be waiting on one. /p /li lipbHand off to automation. /b A protocol that only works when you personally run it isn't finished. /p /li lipbClose the loop. /b Selected designs get expressed and measured on BLI and SPR by the team next door. That tells you whether the selection worked. /p /li /ul h3What We're Looking For /h3 ul lipbMSc or PhD in a relevant field, plus 3+ years running display and selection yourself. /b /p /li lipbYou have set up a display platform, not just used one. This is the core requirement. /b 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. /p /li lipbDepth in at least one display format and working literacy across the rest. /b We are not fixed on which one. /p /li lipbHigh-diversity cloning /b, and an understanding of where bias enters a pool and what it costs you later. /p /li lipbNGS as a routine tool /b, and FACS if you have it. /p /li lipbEnough Python or R to analyze your own data /b without joining a queue. /p /li lipbYou use AI tools seriously. /b It's 2026 and we run on Claude Code across the company. You also need the judgment to check what comes back. /p /li lipbA self-starter. /b Nobody is going to hand you a prioritized queue. You decide what to build, build it, and tell us what you learned. /p /li lipbStartup speed, not academic pace. /b Campaigns run against customer deadlines. A working platform with known limits beats an elegant one next year. /p /li lipbYou want your protocols automated /b rather than manual forever. /p /li /ul pIf 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. /p h3Why This Role Is Interesting /h3 pMost 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. /p pExpression and characterization already run at scale on our automated infrastructure, so you build the selection layer and not everything underneath it. /p h3Details /h3 ul lipbLocation: /b Lausanne, Switzerland, on-site. This is a lab role. /p /li lipbType: /b Full time. /p /li lipbStart date: /b As soon as you can. /p /li /ul pbApplication deadline /b /p pWe are reviewing applicants on a rolling basis. /p /p #J-18808-Ljbffr