Global Quantitative Strategies | Machine Learning Engineer

Overview

Global Quantitative Strategies (GQS) is the quantitative investment business of Citadel. Founded in 2012, GQS has grown into one of Citadel’s core investment strategies and one of the top quantitative investment teams in the world. Collaborative teams of researchers, engineers, and traders develop robust systems and advanced quantitative models to operate at scale and identify investment opportunities across global markets.

Machine Learning Engineers (MLEs) in GQS work at the intersection of deep learning, quantitative research, and high-performance computing. In this role, you will collaborate closely with Quantitative Researchers and Quantitative Research Engineers to design, build, optimize, and scale models and modeling systems that power research and production workflows. This is not a traditional infrastructure engineering role. MLEs are deeply embedded in the research process, partnering with researchers to understand modeling challenges, translate research ideas into scalable model architectures, and improve the performance, reliability, and efficiency of machine learning systems.

You will work on model architecture, distributed training, inference optimization, research tooling, and internal ML libraries that enable the development and deployment of models across major asset products globally. The work directly supports the research and productionization of machine learning models used in systematic investing, including developing new modeling approaches, optimizing large-scale training workflows, and creating tools that help researchers experiment faster and more effectively.

Design, implement, and optimize machine learning models and modeling systems used in research and production workflows.

Collaborate with Quantitative Researchers and Engineers to translate research ideas into scalable model architectures.

Contribute to distributed training, inference optimization, and tooling for ML libraries used across the firm.

Develop and optimize ML workflows for training speed, inference performance, scalability, reliability, and cost efficiency.

Work within Linux-based, high-performance computing or distributed computing environments and ensure robust, maintainable solutions.

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Mathematics, Statistics, Machine Learning, or an equivalent technical field
• Strong programming skills in Python with experience in C++, CUDA, or other performance-oriented technologies
• Experience designing, implementing, training, or optimizing machine learning models, particularly deep learning models
• Strong understanding of model architecture, training dynamics, optimization techniques, and performance tradeoffs
• Experience with PyTorch, Tensor Flow, JAX, or similar ML frameworks
• Experience building or extending ML libraries, research tooling, model training systems, or distributed training workflows
• Ability to optimize ML workflows for training speed, inference performance, scalability, reliability, and cost efficiency
• Experience developing on a Linux stack and working in modern HPC or distributed computing environments
• Ability to collaborate with researchers, understand open-ended research problems, and translate modeling needs into robust technical solutions
• Proven track record of solving complex technical problems with creativity, strong judgment, and attention to research impact
• Strong communication skills and ability to work across research, engineering, and infrastructure teams
• Interest in financial markets and applying ML to systematic investing

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In accordance with applicable law, the base salary range for this role is $275,000 to $350,000. The employee in this role will be eligible to participate in a discretionary incentive compensation program, as well as a wide array of benefit programs, including medical and life insurance, retirement and tax-free savings plans, and access to other healthcare programs.