Few-shot learning Jobs

PostEra is seeking a Machine Learning Researcher to spearhead the development of an agentic research vertical. This role involves leveraging agentic systems to automate the creation of mechanistic models for biochemical and physiological processes, and to analyze biological data for target validation. You will collaborate closely with chemists and biologists to guide drug discovery decisions using these models. A key focus will be on developing machine learning methods capable of rapid adaptation to new drug discovery challenges with limited labeled data, particularly in molecular and tabular in-context learning and building foundation models from proprietary multimodal data. You will be responsible for the entire research loop, from defining tasks and constructing datasets to training and scaling models, performing rigorous ablations, and translating successful methods into practical capabilities for PostEra's scientists. While prior drug discovery experience is not mandatory, a strong motivation to learn the domain and collaborate with interdisciplinary scientists is essential.

This role focuses on agent capability evaluations, benchmark design, LLM-as-judge systems, failure analysis, and the supporting infrastructure. It's a high-growth, high-ownership position on a small team where you will immediately contribute to evaluation infrastructure relied upon by researchers. You will be responsible for running the full evaluation pipeline, building judge calibration protocols, extending existing benchmarks with new tasks, performing failure analysis on model outputs, and owning recurring evaluation workflows and tooling.