You'll be a player-coach — someone who still ships code but also owns the engineering roadmap, team structure, and technical direction. You'll translate company strategy into engineering priorities, make sure the right things get built in the right order, and help a small, high-output team scale without losing speed. You'll lead a small, technical engineering team that leverages AI to build the software infrastructure for running our automated lab. Your job is to make it the best small engineering team in biotech, and grow it thoughtfully as we scale. This is not a management-track role where you stop building. You'll stay hands-on, especially as AI agents reshape what a small engineering team can accomplish. We expect you to be deeply technical, opinionated about architecture, and actively shipping — while also being the person who ensures the whole engineering org is pointed in the right direction and moving fast.
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.
