Deep Learning ML Researcher

Opportunities may be available from time to time in any location in which the business is based for suitable candidates. If you are interested in a career with Citadel, please share your details and we will contact you if there is a vacancy available. Researchers build and deploy models across equities, options, fixed income, and derivatives domains, often working with petabyte-scale data.

Models incorporate advanced techniques: deep learning, sequence / time-series models, representation learning, and methods to tame overfitting and ensure robustness in financial regimes. The goal: each model isn’t a theoretical exercise — it has a measurable P&L impact (or reduces risk / cost) when deployed in live market-making or trading contexts.

Key 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.  Skillset 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 (float
16, bfloat
16), 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 Tensor Flow.  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.