Machine Learning Researcher

Own the full research lifecycle, from hypothesis, experiment design, model validation, risk/overfit controls, to deployment

Conduct cutting-edge research and development in machine learning (e.g. LLMs) at scale with a focus on industry leading techniques and their applications in quantitative finance

Ship models to production that move P&L in options markets—measured by clear, testable outcomes

Prototype → test → iterate fast The resources and support to take great ideas from concept to trading in a very short space of time

Discover alpha in high-dimensional data with deep learning, time-series, and representation learning

Engineer scalable research pipelines from feature generation to distributed training and backtesting

Develop trading intuition to translate insights into executable strategies

Leverage large scale compute and data (petabytes; large budgets) to run ambitious experiments and push the frontier

A curiosity to learn about financial markets, and excitement to understand microstructure, options dynamics, and volatility regimes on the job

Masters or PhD degree in mathematics, statistics, physics, computer science, or another highly quantitative field, with advanced training and a strong research track record working on machine learning problems