Machine Learning Researcher

At Citadel Securities, we are at a once-in-a-generation opportunity in the financial markets. Machine Learning Researchers on our Options team come from a wide range of industries and turn cutting-edge ideas and petabyte-scale data into bleeding edge models with direct trading impact. Our team of researchers iterate quickly, own decisions end-to-end, and operate with substantial autonomy, resources, and scope in a flat, no-bureaucracy environment.

Responsibilities

• 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

Skills and Preferred Qualifications

• 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
• Deep knowledge of cutting edge large scale models and their training and design
• Training techniques (pre training, fine tuning, RL, RLHF), and optimization methods
• A results oriented track record of having taken ML ideas from theory to measurable impact
• Strong math fundamentals (linear algebra, probability, optimization) and mastery of regression/ML for large scale data
• Hands on with modern machine learning (sequence models/transformers, representation learning, regularization, cross validation, causal/robust inference) applied in practice
• Bias to action & problem-solving demonstrated ability and comfort around owning decisions, iterating quickly, and simplifying complex problems to impactful solutions
• Curiosity about markets and enthusiasm to learn microstructure, options dynamics, and volatility regimes on the job
• Fluency in Python (NumPy, PyTorch) and the ability to write clean, modular, performant code for large scale experiments