The role asks for a scientist who can sit between a customer's protein design models and a working lab. That means planning experiments, running or guiding binding and stability measurements, and turning noisy results into a clear next step. A strong candidate has hands-on experience with methods like SPR, BLI or nanoDSF, knows the main protein design approaches, and can explain trade-offs to other researchers. Comfort with Python and taking ownership of outcomes matters just as much.
How it fits the AI era
AI models can now propose new proteins quickly, but someone still has to test them and decide what to try next. This role sits at that handoff, helping customers pick sensible design methods and read real lab data. The human part is judgment: framing a biological question, spotting when a result looks off, and building trust with the research teams.
How it builds AI experience
The work gives close contact with protein language models, diffusion-based design, structure prediction and physics-based tools, used on real customer projects with real lab results coming back. Working with ARIA-funded teams also means helping shape new assays that run on an automated platform. To build this experience beforehand, try design tools on a public target, then analyse binding or stability data in Python notebooks.
Worth showing
- SPR, BLI or nanoDSF experience
- Python data analysis in notebooks
- Assay development or automation
- Clear scientific communication
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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