PpAdaptyv is building an automated lab that lets AI agents run biology experiments. /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’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. /p h3What You’ll Do /h3 ul liBuild orchestration software that coordinates liquid handlers, plate readers, incubators, and robot arms — handling timing dependencies, state, and error recovery. /li liReverse-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. /li liModel and execute complex multi-step protocols reliably — a single run can span dozens of steps across multiple instruments. /li liBuild error-recovery logic so that when something fails mid-run, the system retries, skips, alerts, or pauses depending on the failure mode. /li liCreate monitoring and observability for work‑cell health: instrument status, run progress, error rates. /li liDebug across the software–hardware boundary — figuring out whether bad data is a comms, firmware, calibration, or code problem. /li liWork closely with lab automation engineers, the rest of the software team, and the scientists running production. /li /ul h3Stack /h3 pTypeScript and Python, Postgres (Supabase), Modal for compute. We control instruments with open-source Python tooling like PyLabRobot and PyHamilton wherever we can, rather than proprietary vendor GUIs. /p h3What We’re Looking For /h3 ul liStrong software engineering skills. You write production code in Python and/or TypeScript — well‑structured and maintainable, not just prototypes. /li liComfortable at the hardware‑software boundary. You’ve built software that drives physical devices, or you’re excited to. You can read a protocol spec, debug a flaky connection, and reason about timing. /li liLab automation experience is a strong plus. Familiarity with PyHamilton, PyLabRobot, Opentrons, or similar tooling helps — as does a background in robotics, industrial automation, IoT, or embedded systems. /li liMaker and hacker attitude. You like figuring out how closed systems work and building the thing that makes them work better. Bonus if you’re comfortable with electronics, microcontrollers, or a 3D printer when an integration needs a physical fix. /li liAI‑native builder. It’s 2026 — you build with coding agents like Claude Code as a default, and you have sharp judgment about what they produce. /li liSelf‑starter and independent. You define what needs building from how the lab actually works, not just what’s in the ticket. /li liReliability‑minded. The lab runs 24/7; you design systems where one instrument failing doesn’t cascade through the whole work cell. /li /ul pBiology background not required — but you should be excited that the code runs real experiments. /p h3Details /h3 ul liLocation: Lausanne, Switzerland (on‑site — you need hands‑on access to physical instruments). /li liType: Full time /li liStart date: ASAP /li /ul h3Application deadline /h3 pWe are reviewing applicants on a rolling basis. /p /p #J-18808-Ljbffr