Post-Training Research Scientist - Forecasting and Reasoning

We develop and use LLMs for forecasting and decision-making in financial markets. Our research combines structured time series, unstructured information, and computational tools to study how models acquire useful evidence, make calibrated predictions, and revise their beliefs as new information arrives. We rely on proprietary datasets, domain expertise, and real investment applications to evaluate these capabilities. We deal with rich, challenging, noisy, non-stationary data. Our work focuses on advances in training environments, reward design, policy optimization, post-training and evaluation.

We are looking for an exceptional post-training research scientist to develop methodologies across environment and state construction, data generation and mixing, training, and evaluation. Working closely with other researchers, you will help shape the research agenda and translate successful methods into production. Specifically, you will: