AI Biologist - Microscopy

About the Role

You will be the scientific authority on ground truth, testing whether agents can reason like an expert microscopist. AI agents today struggle to interrogate raw images, separate real biology from artifacts in acquisition, staining, and segmentation, and reach conclusions an expert would stand behind. Experts themselves disagree on roughly 1 in 6 images, so knowing where the evidence ends is part of the work. You will work with software engineers and scientists to build the tasks and datasets behind our benchmarks across cell biology, tissue, and disease. Your judgment will set the standard agent outputs are held to.

Build the task

• Define what expert reasoning looks like for an imaging question, and write gold-standard answers with the rationale an experienced microscopist or pathologist would defend
• Design realistic scenarios across the imaging workflow: sample prep, staining, acquisition, preprocessing, segmentation, quantification, and interpretation
• Curate image datasets across modalities: immunofluorescence, confocal and live-cell, electron microscopy, histology and histopathology, and high-content screening
• Build analysis pipelines in Python, including deep-learning segmentation, that produce verifiable ground truth
• Partner with software engineers to turn your judgment into new workflows

Set the hard calls

• Test whether agents distinguish true signal from artifact, such as bleed-through, uneven illumination, fixation damage, out-of-focus planes, or segmentation failure
• Adjudicate ambiguous images: decide what the data supports, where reasonable experts would disagree, and which additional stain, image, or experiment would settle the call
• Test whether agents choose the right imaging modality, resolution, and unit of analysis for the question

Judge the agent

• Interrogate agent reasoning step by step, and challenge conclusions that sound right but would not survive pathologist review or lab meeting
• Pinpoint where agents go wrong: why a step fails, what an expert would do instead, and what evidence would change the call
• Score agent confidence, rewarding explicit uncertainty over overconfident calls on ambiguous images

Requirements

• PhD in cell biology, bioimage analysis, pathology, or a related field; or an MD with pathology training
• Senior, hands-on imaging experience: you have looked at thousands of samples and know what normal and abnormal look like
• Direct experience with image segmentation tools such as CellProfiler, Cellpose, StarDist, QuPath, ilastik, napari, or Fiji
• Deep expertise in at least one area (bioimage analysis, cell biology, or histopathology) and working fluency across the others
• Strong command of experimental design, image quantification, and statistics
• AI-ready from day one: daily use of Claude, Cursor, or similar agents, with the judgment to spot where model reasoning is technically correct but scientifically unsound
• Confidence to overrule an AI agent when the evidence demands it, and the openness to update when shown wrong
• Ability to calibrate confidence and communicate uncertainty rather than defaulting to binary calls
• Bonus: electron microscopy, pathology, or pharmacology experience (e.g., pharmacodynamic or drug-uptake imaging assays)
• Bonus: familiarity with public resources such as the Image Data Resource (IDR), BioImage Archive, Human Protein Atlas, Cell Painting Gallery, or TCGA whole-slide images