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
We build our lab automation ourselves. Instead of buying turnkey systems and running them through a vendor’s GUI, we reverse-engineer instruments, control them with open-source Python tooling like PyLabRobot and PyHamilton, and build the software and hardware to turn a pile of instruments into work cells that run biology around the clock.
We’re looking for a Lab Automation Engineer to help design, build, and run these systems. This is a broad role: on any given week you might be writing a driver for a new instrument, getting a liquid handler and a robot arm to hand off cleanly, or debugging why an overnight run stalled. We care far more about range, ability, and drive than years on any one platform.
 
What You’ll Do

• Control lab instruments — liquid handlers, robot arms, plate readers, incubators, analytical devices — programmatically in Python, ideally with PyLabRobot / PyHamilton, rather than locking workflows into proprietary software.

• Build and integrate work cells: get multiple instruments working together as one orchestrated unit that runs unattended.

• Bring new instruments online by reverse-engineering their control protocols and building drivers that expose them to the rest of the stack.

• Write the software glue — drivers, integrations, small services — that connects hardware to our platform (mostly Python, with some TypeScript).

• Partner with biologists to turn new assays into reliable automated workflows, then keep them running: diagnosing and fixing the mechanical, electrical, and software failures that come with 24/7 production.

• Push throughput and reliability up continuously — better error recovery, smarter scheduling, sturdier hardware.

 
What We’re Looking For

• Maker and hacker attitude. You like taking systems apart, figuring out how they work, and building the thing that makes them work better. You’d rather script an instrument than click through it.

• Strong software engineering skills. You write real Python and can structure code others can maintain — drivers, integrations, services — not just throwaway scripts. You’ll pick up TypeScript where needed.

• Lab automation experience. Hands-on with PyHamilton, PyLabRobot, Opentrons, or similar tooling is a strong plus. Direct experience automating instruments and workflows matters more than the specific platform.

• Self-starter and independent. You see what needs building and build it. You don’t wait for a spec, and you don’t need a vendor training course to get going.

• 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.

• Hardware comfort is a plus. Electronics, microcontrollers, 3D printing, CAD, or fabrication experience all help, but they’re not required if your software and automation skills are strong.

• Debugging instinct. When something fails mid-run, you reason across software, firmware, electronics, and mechanics to find the cause and fix it.

Biology background not required — but you should be excited that the robots run real experiments.
 
Details

• Location: Lausanne, Switzerland (on-site — you’re building physical systems)

• Type: Full time

• Start date: ASAP

Application deadline
We are reviewing applicants on a rolling basis.

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
We build our lab automation ourselves. Instead of buying turnkey systems and running them through a vendor’s GUI, we reverse-engineer instruments, control them with open-source Python tooling like PyLabRobot and PyHamilton, and build the software and hardware to turn a pile of instruments into work cells that run biology around the clock.
We’re looking for a Lab Automation Engineer to help design, build, and run these systems. This is a broad role: on any given week you might be writing a driver for a new instrument, getting a liquid handler and a robot arm to hand off cleanly, or debugging why an overnight run stalled. We care far more about range, ability, and drive than years on any one platform.
 
What You’ll Do

• Control lab instruments — liquid handlers, robot arms, plate readers, incubators, analytical devices — programmatically in Python, ideally with PyLabRobot / PyHamilton, rather than locking workflows into proprietary software.

• Build and integrate work cells: get multiple instruments working together as one orchestrated unit that runs unattended.

• Bring new instruments online by reverse-engineering their control protocols and building drivers that expose them to the rest of the stack.

• Write the software glue — drivers, integrations, small services — that connects hardware to our platform (mostly Python, with some TypeScript).

• Partner with biologists to turn new assays into reliable automated workflows, then keep them running: diagnosing and fixing the mechanical, electrical, and software failures that come with 24/7 production.

• Push throughput and reliability up continuously — better error recovery, smarter scheduling, sturdier hardware.

 
What We’re Looking For

• Maker and hacker attitude. You like taking systems apart, figuring out how they work, and building the thing that makes them work better. You’d rather script an instrument than click through it.

• Strong software engineering skills. You write real Python and can structure code others can maintain — drivers, integrations, services — not just throwaway scripts. You’ll pick up TypeScript where needed.

• Lab automation experience. Hands-on with PyHamilton, PyLabRobot, Opentrons, or similar tooling is a strong plus. Direct experience automating instruments and workflows matters more than the specific platform.

• Self-starter and independent. You see what needs building and build it. You don’t wait for a spec, and you don’t need a vendor training course to get going.

• 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.

• Hardware comfort is a plus. Electronics, microcontrollers, 3D printing, CAD, or fabrication experience all help, but they’re not required if your software and automation skills are strong.

• Debugging instinct. When something fails mid-run, you reason across software, firmware, electronics, and mechanics to find the cause and fix it.

Biology background not required — but you should be excited that the robots run real experiments.
 
Details

• Location: Lausanne, Switzerland (on-site — you’re building physical systems)

• Type: Full time

• Start date: ASAP

Application deadline
We are reviewing applicants on a rolling basis.