The AI Biologist will contribute to benchmarks, strategy, and research on agents designed to diagnose and aid rare disease research. They will structure and curate complex genomic and clinical data while partnering with engineers to define new data workflows.
Design and build multi-stage tasks to benchmark AI agents on real-world spatial transcriptomics projects. Evaluate agent decision-making processes across complex biological data workflows from image alignment to downstream inference.
You will identify cancer-biology datasets and transform them into rigorous, deterministically-graded tasks for AI agents. Your role involves establishing defensible reference analyses and scoring criteria to evaluate agent performance across various clinical and biological workflows.
You will contribute to the technical approach for evaluating how AI agents reason through complex microbiome and metagenomics datasets. Additionally, you will collaborate with engineers and biologists to build ground-truth benchmark datasets that test the analytical capabilities of AI agents.
Contribute to the technical approach of teaching AI agents to handle complex drug discovery and development data. Work across end-to-end workflows from instrument outputs to scientific decisions regarding genetic targets and drug design.
Develop technical approaches to evaluate AI agents' reasoning in biological functions and build ground-truth benchmark datasets from real discovery programs. Collaborate with software engineers and biologists to measure agent capabilities in understanding biological sequences and structures.
Develop technical approaches and ground-truth benchmark datasets to evaluate AI agents' reasoning in biologics therapeutics. Collaborate with software engineers and biologists to measure agent capability in therapeutic discovery and decision-making.
Contribute to the technical approach of teaching AI agents to understand and handle complex genomic and proteomic data. Work across end-to-end data analysis workflows from instrument outputs to scientific decision-making.
Contribute to the technical approach of teaching AI agents to understand and handle complex genomic data. Work on end-to-end data analysis workflows ranging from instrument outputs to scientific decision-making.
