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 (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.