Quantitative AI Technical Staff
Citadel SecuritiesAbout the Role
Citadel Securities is one of the largest market makers in the world, providing liquidity across nearly every major asset class and market globally in real time, and machine learning is fundamental to how we do it. As part of the firm's centralized applied-AI team, you will conduct frontier model research on the deep-learning models that predict where prices are going and tailor that research to the highest-impact strategies.
- Design and train deep-learning models that predict price movements, developing new architectures, improving features, and extending how far into the future models can reliably predict.
- Build the systems and tooling that enable researchers across the firm to apply machine learning to their own problems.
- Tackle novel problems in high-impact markets where established solutions do not yet exist.
- Apply AI across the full chain from prediction to trading decisions, including simulating how a set of trades would perform before capital is committed.
The role is approximately half research and half engineering, with substantial independence to pursue the research directions you believe are most impactful, supported by state-of-the-art clusters and dedicated engineering, hardware, and systems teams. There is no prior finance or markets experience required.
Responsibilities
- Model research: design and train the deep-learning models that predict where prices are going — developing new architectures, improving features, and extending how far into the future the models can reliably predict.
- Enabling ML across the firm: build the systems and tooling that allow many other researchers to apply machine learning to their own problems, multiplying your impact well beyond your own work.
- High-impact markets: take on novel problems in markets that generate billions of dollars in annual revenue, where established solutions do not yet exist.
- From prediction to trading decisions: apply AI across the full chain — including simulating how a set of trades would perform before any capital is committed, and a growing range of decision problems beyond prediction itself.
- Improve every part of our models — from how we featurize raw market data, to architecture design, to training dynamics, to how a model's predictions become trading decisions.
- Direct several coding agents in parallel — to run experiment sweeps, refactor subsystems, or investigate issues across a large codebase.
Requirements
- Strong software engineering fundamentals and ability to write clean, efficient, correct code
- Solid depth in machine learning and deep learning, with hands-on experience training and debugging models
- Results-oriented, with a bias toward action, flexibility, and efficiency
- Genuinely curious about how markets work and why trades are profitable, even without a prior finance background
- Communicate clearly and collaborate effectively across a large, technical organization
- Coachable and responsive to feedback; growth trajectory valued over starting point
- Comfortable working in an AI-native way, directing multiple agents to carry out research and engineering, or eager to learn
- Bachelor's, Master's, or PhD - or an equivalent combination of education, training, and experience - in a relevant field (computer science, mathematics, physics, electrical engineering, statistics, or similar)
- Entry-level experience; graduates across all degree levels are encouraged to apply