AI Engineer
Adaptyv
Lausanne, VD, CH
Onsite
2026-06-25
Estimated salary · Lausanne
~ CHF 294,047 - CHF 402,380
Low
CHF 98K
Median
CHF 115K
High
CHF 135K
Market in Lausanne · BFS 2025
Job description
**Adaptyv is building an automated lab thats let 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**
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We already use AI across every part of the company — business operations automation, data analysis and reporting, AI\-driven review of customer experiment data, agentic workflows for lab scheduling and customer communication, and a lab\-wide assistant the team leans on. The capabilities largely exist. What's missing is someone whose entire job is taking what we've already built and making it successful: wrapped, installable, wired into the tools people use every day, and turned into the default way the company works.
This is an internal\-facing role focused on process optimization. You won't spend most of your time inventing new features — you'll take the capabilities that already exist across LabOS, our internal APIs, and our