Life Sciences PhD Expert, AI Agent Evaluation

What you'll do: You'll take problems from your own research and turn each one into a self-contained task that an AI agent has to solve in a Linux terminal. For every task, you write a clear brief, set up the environment with the data and tools the agent needs, build a reference solution, and write automated checks on the final result. The strongest tasks are the ones today's best agents still get wrong, so your firsthand knowledge of where real life-science work goes wrong is what makes them valuable. Specialties we're looking for If your PhD is in a life-science field or a closely related field (for example, biomedical engineering, chemistry, or biostatistics), there's likely a place for you. We're building depth across genomics, molecular and cell biology, immunology, neuroscience, biochemistry, pharmacology, microbiology, bioinformatics, and computational biology, among others. Who we're looking for No AI or machine-learning background needed. Your life-science expertise and your comfort working in a terminal are what we're after.

Requirements: You hold, or are completing, a PhD in a life-science field or a closely related field (for example, biomedical engineering, chemistry, or biostatistics).You've done computational work in your field and are comfortable in a Linux terminal: shell commands, scripting in Python or Bash, installing packages. You've used Docker, or can pick it up quickly. We share templates and worked examples. You can draw on your own research, code, and data as source material for your tasks. You have a precise, adversarial eye and enjoy finding the exact point where a workflow breaks, then explaining it cleanly. You're honest about uncertainty and write instructions that leave no room for misreading. You're based in the US or Canada. Background in genomics, bioinformatics, or computational biology is a plus. Materials you can use Almost any part of your technical work can seed a task, for example: Analysis code, scripts, and pipelines from your research Datasets, supplementary files, and the processing behind them Figures and results you can reproduce from raw data Methods and protocols you've run computationally Bugs, edge cases, and failure modes you've had to debug yourself Sequences, alignments, structures, and imaging data