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Quantitative Research Engineers at Citadel's GQS develop software solutions, enhance trading systems, and collaborate with researchers to apply quantitative techniques.

As a Quantitative Developer, you'll innovate and enhance quantitative methods, develop custom tools, maintain data pipelines, and collaborate with researchers for advancing investment strategies.

The role involves physical gas trading, analysis, and scheduling, requiring keen knowledge of U.S. gas markets and collaboration with internal stakeholders.

Refers candidates for Software Engineering roles at Citadel, focusing on innovative technology and teamwork in financial systems.

As a PhD intern, you will design and develop software solutions for automated trading systems, collaborating closely with the Quantitative Research team.

SREs at Citadel focus on optimizing and maintaining system reliability, performance, and automation for investment applications, collaborating closely with teams.

Optimize and manage portfolio-level trading strategies by translating quantitative research into production. Tune allocators and risk, minimize trading costs and market impact, monitor performance in real time, and conduct statistical research to improve risk-adjusted returns.

Commodities Analysts generate commodity investment ideas through fundamental research and data analysis, focusing on modeling supply and demand and improving data quality.

Develop and deploy advanced machine learning models across asset classes, focusing on deep learning, sequence/time-series modeling, NLP/LLMs, pre-training, fine-tuning, and reinforcement learning. Design and optimize models for performance, scalability, and robustness to model price formation, market behavior, and risk while collaborating with researchers, engineers, and traders.

Partner with quantitative researchers to design, build, optimize, and scale deep learning models and tooling for research and production. Work includes model architecture, distributed training, inference optimization, ML libraries, and high-performance computing to improve training speed, scalability, reliability, and cost-efficiency in systematic investing workflows.