Software Engineer, Lab Automation

Job description

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'll build work-cell orchestration, instrument drivers, protocol scheduling, error-recovery logic, and monitoring. Physical systems fail in ways pure software doesn't — a plate gets stuck, a liquid handler skips a well, a temperature controller drifts. Your job is to make the system handle all of it gracefully. This is a broad, hands-on role for a strong engineer who wants their code to drive real machines and see it run the same day. What You'll Do Build orchestration software that coordinates liquid handlers, plate readers, incubators, and robot arms — handling timing dependencies, state, and error recovery. Reverse-engineer and develop instrument drivers and APIs. Each instrument speaks a different protocol (serial, USB, TCP/IP); you work out how it talks and build a clean abstraction over it. Model and execute complex multi-step protocols reliably — a single run can span dozens of steps across multiple instruments. Build error-recovery logic so that when something fails mid-run, the system retries, skips, alerts, or pauses depending on the failure mode. Create monitoring and observability for work-cell health: instrument status, run progress, error rates. D…