About the role You'll work across LabOS — our internal software platform — building the orchestration layer that coordinates everything from experiment scheduling to execution to data capture and results processing. This is full-stack product engineering at the intersection of software and physical-world biology. Day-to-day, that can mean things like: Creating interfaces and APIs that give scientists and AI agents visibility into what's happening in the lab in real time Building custom AI agents and tooling that automate decisions across the experiment lifecycle, from protocol design to troubleshooting failed results Turning proprietary hardware into API-controllable devices that agents and software can operate programmatically Designing scheduling systems that coordinate dozens of lab instruments with complex dependency chains You'll own large areas of the product. We're a small team where individual engineers have large impact on what gets built and how.
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.
