Global Quantitative Strategies (GQS) | Machine Learning Researcher

Job Description
About Global Quantitative Strategies (GQS)
GQS is the quantitative investment business of Citadel. Founded in 2012, GQS has grown rapidly to become 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 build robust systems that operate at scale and apply advanced quantitative and machine learning techniques to identify investment opportunities.

Machine Learning Researchers at GQS develop and deploy models across a variety of asset classes. Their work spans deep learning, sequence and time-series modeling, natural language processing, large language models, pre-training, reinforcement learning, and methods for improving robustness in complex financial regimes.

Location
New York, Singapore

Required Skills and Qualifications:

Advanced degree in Computer Science, Machine Learning, Mathematics, Statistics, Engineering, Physics, or a related quantitative field
Proven ability to conduct innovative and impactful research focused on solving real-world problems
Deep expertise in machine learning and deep learning, such as sequence modeling, large language models, or reinforcement learning
Experience with modern training techniques such as pre-training, fine-tuning, reinforcement learning, or related optimization methods
Expertise in Python and machine learning frameworks such as PyTorch or JAX
Strong mathematical and statistical foundations
Ability to design, implement, and optimize machine learning models for performance, scalability, and robustness
Demonstrated interest in financial markets and a drive to apply machine learning techniques to model price formation, market behavior, and risk

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In accordance with applicable law, the base salary range for this role is $300,000 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.
About Citadel
Citadel is one of the world’s leading alternative investment managers. We manage capital on behalf of many of the world’s preeminent private, public and nonprofit institutions. We seek the highest and best use of investor capital in order to deliver market leading results and contribute to broader economic growth. For over 30 years, Citadel has cultivated a culture of learning and collaboration among some of the most talented and accomplished investment professionals, researchers and engineers in the world. Our colleagues are empowered to test their ideas and develop commercial solutions that accelerate their growth and drive real impact.

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"description": "About Global Quantitative Strategies (GQS)\nGQS is the quantitative investment business of Citadel. Founded in 2012, GQS has grown rapidly to become one of Citadel\u2019s core investment strategies and one of the top quantitative investment teams in the world. Collaborative teams of researchers, engineers, and traders build robust systems that operate at scale and apply advanced quantitative and machine learning techniques to identify investment opportunities.\n\nMachine Learning Researchers at GQS develop and deploy models across a variety of asset classes. Their work spans deep learning, sequence and time-series modeling, natural language processing, large language models, pre-training, reinforcement learning, and methods for improving robustness in complex financial regimes.\n\nLocation\nNew York, Singapore\n\nRequired Skills and Qualifications:\n\nAdvanced degree in Computer Science, Machine Learning, Mathematics, Statistics, Engineering, Physics, or a related quantitative field\nProven ability to conduct innovative and impactful research focused on solving real-world problems\nDeep expertise in machine learning and deep learning, such as sequence modeling, large language models, or reinforcement learning\nExperience with modern training techniques such as pre-training, fine-tuning, reinforcement learning, or related optimization methods\nExpertise in Python and machine learning frameworks such as PyTorch or JAX\nStrong mathematical and statistical foundations\nAbility to design, implement, and optimize machine learning models for performance, scalability, and robustness\nDemonstrated intere