A software engineer in lab automation typically needs solid programming skills plus the ability to connect code to physical instruments: writing drivers, scheduling protocols, and handling failures when hardware misbehaves. A strong candidate brings care for reliability and monitoring, and comfort debugging problems that only show up on real equipment. At a lab that tests AI designed proteins, that means keeping experiments running correctly so the data researchers receive can be trusted.
How it fits the AI era
AI can propose protein designs and suggest protocol changes, but someone has to turn those into instructions that real machines execute safely and recover when a plate sticks or a well is skipped. The work shifts toward supervising automated runs, building error recovery, and checking that results match expectations. Judgment about when a run is invalid and what to do next stays human.
How it builds AI experience
This role builds practical AI experience by working alongside AI designed proteins and seeing where model output meets physical reality. A candidate can strengthen this by learning how automated experiments generate training data and how to log and monitor runs so failures are caught early. Familiarity with instrument control and data pipelines helps connect AI proposals to verified lab results.
Worth showing
- Error recovery design
- Run monitoring and logging
- Python for automation
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.
Tools mentioned: Python, TypeScript, embedded systems, robotics
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