Deep Learning ML Researcher

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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.