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Metro location, popular ML researcher title, and mid-level seniority increase competitive density.
Core ML research skills transfer across industries, but market microstructure and trading specifics increase domain sensitivity.
Technical research expertise, GPU/LLM experience, and finance domain knowledge imply rigorous, selective screening.
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Research and analyze large-scale financial market datasets to develop AI/ML models focusing on pattern discovery, prediction, and signal generation.
Conduct experimentation and fine-tuning of large neural networks and LLMs on GPU clusters, including benchmarking and performance evaluation.
Collaborate with quantitative researchers, traders, and engineers to translate research into practical trading use cases and contribute to building reusable AI/ML research tools.
Strong foundation in mathematics, statistics, probability, linear algebra, optimization, and machine learning.
Proficiency in Python programming and experience with relevant ML libraries (NumPy, Pandas, Scikit-learn).
Experience with handling large datasets, running experiments, and model validation in noisy, non-stationary environments.
Work Experience Required: Not explicitly mentioned in the JD.
Comfortable working on open-ended, research-intensive problems involving complex and noisy financial market data.
Ability to independently structure research problems, conduct rigorous experiments, and communicate findings clearly to technical and business stakeholders.
Experience or interest in quantitative finance domains such as market microstructure, pricing, and execution is a plus but not mandatory.