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Citadel Securities

Deep Learning ML Researcher

Reposted 2 Days Ago
In-Office
Miami, FL
Entry level
In-Office
Miami, FL
Entry level
Conduct research and develop machine learning models for finance, focusing on deep learning and large language models, optimizing them for performance, and collaborating with teams to integrate ML solutions.
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Job Description
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.

About Citadel Securities
Citadel Securities is a technology-driven, next-generation global market maker. We provide institutional and retail investors with world-class liquidity, competitive pricing and seamless front-to-back execution in a broad array of financial products. Our teams of engineers, traders and researchers harness leading-edge quantitative research and the accelerating power of compute, machine learning and AI to power our analytics and tackle the market's and our clients' most critical challenges. Together, we are forging the future of capital markets. For more information, visit citadelsecurities.com .

Citadel Securities Chicago, Illinois, USA Office

353 N Clark St, Chicago, IL, United States, 60654

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