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Citadel
Global Quantitative Strategies | Machine Learning Engineer
Citadel
New York•On-site••3-5 Yrs•Jul 6
Job description
Machine Learning EngineerQuantitative ResearchDeep LearningProduction SystemsFinancial MarketsModel OptimizationResearch Collaboration
Overview
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.
Responsibilities
- Collaborate with researchers on modeling challenges
- Translate research ideas into scalable model architectures
- Improve performance, reliability, and efficiency of ML systems
- Work on model architecture, distributed training, inference optimization
- Develop internal ML libraries that enable development and deployment of models
- Support research and productionization of machine learning models used in systematic investing
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
Education
- Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or Machine Learning
- Master's or PhD degree preferred
Benefits
- Discretionary incentive compensation program
- Medical insurance
- Life insurance
- Retirement plans
- Tax-free savings plans
- Healthcare programs