Global Quantitative Strategies (GQS) | Machine Learning Engineer

About Global Quantitative Strategies (GQS)

Global Quantitative Strategies is the quantitative investment business of Citadel. Founded in 2012, GQS has rapidly 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 work together to develop robust systems and advanced quantitative models that allow us 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 the 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 the development of new modeling approaches, the optimization of large-scale training workflows, and the creation of tools that help researchers experiment faster and more effectively.

Skills and Qualifications

• Bachelor's, Master's, or PhD degree in Computer Science, Engineering, Mathematics, Statistics, Machine Learning, or an equivalent technical field
• Strong programming skills in Python and experience with 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 working with PyTorch, TensorFlow, JAX, or similar machine learning frameworks
• Experience building or extending machine learning 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 high-performance computing or distributed computing environments
• Ability to collaborate closely 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 the ability to work effectively across research, engineering, and infrastructure teams
• Interest in financial markets and applying machine learning 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.

In addition, the employee who fills this role will be eligible to participate in a discretionary incentive compensation program, as well as a wide array of benefit programs, such as medical and life insurance, retirement and tax-free savings plans, and access to other healthcare programs.