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:

• →Develop post-training methods for forecasting and reasoning. Design and execute supervised fine-tuning, reinforcement learning, on-policy distillation, and related methods to improve accuracy and calibration.
• →Build environments and task distributions. Construct point-in-time environments combining historical observations, documents, prior predictions, and tools. Define tasks that require information gathering, belief updating, and forecasting across multiple horizons.
• →Develop reward modeling and credit assignment methods, combining verifiable intermediate outcomes, expert supervision, preference data, and delayed feedback.
• →Build efficient, reliable training systems. Collaborate with engineers to develop synchronous and asynchronous learning pipelines on multi-node GPU clusters.
• →Establish rigorous evaluations that measure forecasting accuracy, uncertainty calibration, reasoning quality, robustness, and downstream utility. Design temporal holdouts and controlled ablations that distinguish generalization from leakage, distribution shifts, and evaluator noise.
• →Productionize research. Collaborate with quantitative researchers to identify valuable model capabilities and deploy successful methods into reliable production systems.

• Bachelor’s degree or higher in a quantitative or technical discipline, or equivalent practical experience developing advanced learning systems.
• Demonstrated expertise in large language model (LLM) post-training, reinforcement learning, environment design, or preference learning. Experience working in non-fully verifiable domains is preferred.
• Hands-on experience with distributed GPU training, including performance optimization, numerical stability, checkpointing, and reproducibility.
• Strong implementation skills in Python and PyTorch or JAX for research and production model training.
• Experience using distributed training and inference frameworks, including FSDP, DeepSpeed, SGLang, Megatron, verl, or Slime.
• Ability to design rigorous evaluations for forecasting accuracy, calibration, reasoning quality, robustness, temporal holdouts, and leakage detection.
• Experience with probabilistic forecasting, time-series modeling, asynchronous reinforcement learning (RL), tool-using agents, or production post-training systems from academic research, frontier AI labs, or teams developing advanced learning systems.
• Commitment to the highest ethical standards.

We invest in our people, their careers, their health, and their well-being. When you work here, we provide:

• Fully-paid health care benefits
• Generous parental and family leave policies
• Volunteer opportunities
• Support for employee-led affinity groups representing women, people of color and the LGBT+ community
• Mental and physical wellness programs
• Tuition assistance
• A 401(k) savings program with an employer match and more

Point72 is a leading global alternative investment firm led by Steven A. Cohen. Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies. We aim to attract and retain the industry's brightest talent by cultivating an investor-led culture and committing to our people's long-term growth. For more information, visit https://point72.com/.