Machine Learning Researcher - Agentic Science

PostEra is an AI-first biotech company developing AI systems to accelerate medicine discovery. The Machine Learning Researcher will develop agentic systems and machine learning methods for biochemical modeling, biological data analysis, molecular and tabular in-context learning, and foundation models, while translating research into tools for drug discovery scientists.

Responsibilities:

• Agentic Research for Chemistry and Biology: Develop and benchmark agentic systems to automate the development of quantitative models to simulate biological and physiological processes, and the analysis of biological data
• Research Direction and Execution: Independently identify, formulate, and lead research projects involving in-context learning, agentic systems, few-shot adaptation, tabular foundation models, and molecular machine learning
• In-Context Learning for Molecules: Design and train models that adapt to new assays, endpoints, targets, or chemical series using limited labeled context and heterogeneous historical data
• Benchmarking: Rigorously compare new approaches against strong baselines, curating test cases that deconfounds impact of different sources of bias
• Cross-Functional Collaboration: Work with scientists to connect modeling objectives and evaluation metrics to practical decisions in potency modeling, ADME prediction, selectivity, lead optimization, and the design of early clinical studies
• Model Training and Scaling: Develop efficient training and data pipelines and, where appropriate, scale models across large collections of molecular and tabular tasks
• Research Engineering: Produce readable, reproducible research code; maintain well-tracked experiments; and contribute through code review, documentation, and shared modeling infrastructure
• Scientific Dissemination: Publish results in leading machine learning, medicinal chemistry, or computational biology venues. You will be an ambassador of PostEra to the scientific community