The role is a hands-on mix of Python engineering and lab hardware work. The person controls instruments such as liquid handlers, robot arms and plate readers through code, links them into work cells that run unattended, and fixes the mechanical, electrical and software problems that appear during round-the-clock runs. A strong candidate writes maintainable code, enjoys taking systems apart, debugs across software and hardware, and works independently with biologists.
How it fits the AI era
AI models can now design proteins, but the physical experiments still need someone to build and keep the machines running. This role sits at that gap, and coding agents are expected to be part of everyday work, with good judgment needed to check what they produce. What stays human is the hands-on troubleshooting, the reverse-engineering of instruments, and the decision about what is reliable enough to run overnight.
How it builds AI experience
The work gives direct exposure to how AI-designed proteins get tested in the real world, since the lab turns sequences from researchers and AI agents into experimental data. Day to day, the person would build with coding agents and help create the physical layer that lets AI systems run experiments. Someone preparing could practice with open-source tools like PyLabRobot, and try using a coding agent to write and review instrument drivers.
Worth showing
- Python driver or integration projects
- PyLabRobot or PyHamilton experience
- Lab instrument automation work
- Building with coding agents
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
About Adaptyv Bio
An automated lab that tests AI-designed proteins, so researchers can send a sequence and get real experimental data back.
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