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Deep Learning ML Researcher

Citadel Securities
🇺🇸MiamiSeniorFull-timePosted March 24, 2026

About the Role

Researchers build and deploy models across equities, options, fixed income, and derivatives domains, often working with petabyte-scale data. Models incorporate advanced techniques including deep learning, sequence/time-series models, representation learning, and methods to ensure robustness in financial regimes. Each model has measurable P&L impact or reduces risk/cost when deployed in live market-making or trading contexts.

  • Conduct cutting-edge research and development in machine learning, with a focus on large language models (LLMs) and deep learning applications in quantitative finance
  • Design, implement, and optimize machine learning models for performance and scalability, particularly in financial contexts
  • Collaborate with cross-functional teams to integrate ML solutions into business processes and trading strategies
["Proficiency in creating and using algorithms to investigate and work through large data or error-checking problems", "Deep knowledge of LLM architectures, including transformers", "Familiarity with attention mechanisms, normalization techniques, and model architecture design", "Training techniques (pre-training, fine-tuning, RLHF) and optimization methods", "Understanding of low-level details like GPU memory management, precision types (float16, bfloat16), and parallelization techniques", "Proficiency in advanced training techniques such as pre-training, fine-tuning, RLHF, and DPO", "Expertise in Python and ML frameworks like PyTorch or TensorFlow", "Familiarity with Retrieval Augmented Generation (RAG) systems and their implementation", "Proven ability to approach open-ended problems and design end-to-end solutions in ML/AI", "Strong mathematical and statistical foundations, particularly in areas relevant to quantitative finance"]

Responsibilities

  • Conduct cutting-edge research and development in machine learning, with a focus on large language models (LLMs) and deep learning and their applications in quantitative finance
  • Design, implement, and optimize machine learning models for performance and scalability, particularly in financial contexts
  • Collaborate with cross-functional teams to integrate ML solutions into business processes and trading strategies
  • Build and deploy models across equities, options, fixed income, and derivatives domains, often working with petabyte-scale data

Requirements

  • Proficiency in creating and using algorithms to meticulously investigate and work through large data or error-checking problems
  • Deep knowledge of LLM architectures, including transformers.
  • Familiarity with attention mechanisms, normalization techniques, and model architecture design.
  • Training techniques (pre-training, fine-tuning, RLHF), and optimization methods.
  • Understanding of low-level details like GPU memory management, precision types (float16, bfloat16), and parallelization techniques.
  • Proficiency in advanced training techniques such as pre-training, fine-tuning, RLHF, and DPO.
  • Expertise in Python and ML frameworks like PyTorch or TensorFlow.
  • Familiarity with Retrieval Augmented Generation (RAG) systems and their implementation.
  • Proven ability to approach open-ended problems and design end-to-end solutions in ML/AI.
  • Strong mathematical and statistical foundations, particularly in areas relevant to quantitative finance.