Adaptyv
Software Engineer, Lab Automation
1mo ago
Europesoftware engineeringapihardware integrationprotocol schedulingerror recovery
Build work-cell orchestration, instrument drivers, protocol scheduling, error-recovery logic, and monitoring for automated lab systems.
Responsibilities
- 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.
- 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.
- Debug across the software–hardware boundary — figuring out whether bad data is a comms, firmware, calibration, or code problem.
- Work closely with lab automation engineers, the rest of the software team, and the scientists running production.
Requirements
- Strong software engineering skills. You write production code in Python and/or TypeScript — well-structured and maintainable, not just prototypes.
- Comfortable 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.
- Lab 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.
- Maker 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.
- AI-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.
- Self-starter and independent. You define what needs building from how the lab actually works, not just what's in the ticket.
- Reliability-minded. The lab runs 24/7; you design systems where one instrument failing doesn't cascade through the whole work cell.
- Biology background not required — but you should be excited that the code runs real experiments.
Other
- 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.
- TypeScript 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.
- Location: Lausanne, Switzerland (on-site — you need hands-on access to physical instruments).
- Type: Full time
- Start date: ASAP
- We are reviewing applicants on a rolling basis.