We are seeking a highly skilled AI / Machine Learning Engineer to join a dynamic quantitative research and trading team at a leading hedge fund. The ideal candidate will bring deep expertise in Python, machine learning, and recent experience working with Large Language Models (LLMs). You will work closely with data scientists, PMs, and software engineers to build AI-driven systems that enhance investment decision-making and drive alpha generation.
Key Responsibilities:
- Develop, fine-tune, and deploy Large Language Models (LLMs) to extract insights from financial text data (e.g., earnings calls, broker reports, regulatory filings, internal research).
- Collaborate with researchers and PMs to identify AI use cases that can improve investment strategies and operational workflows.
- Design and implement scalable ML pipelines in Python, ensuring reproducibility and robustness across datasets and environments.
- Integrate LLM outputs with structured financial data to support portfolio construction, signal generation, and risk management workflows.
- Own model evaluation, optimization, and monitoring, ensuring that deployed systems are stable, interpretable, and production-ready.
- Contribute to the selection and experimentation of open-source models and fine-tuning strategies (e.g., prompt engineering, supervised fine-tuning, RAG pipelines).
- Stay current on advances in NLP/ML research and assess their practical impact on the fund’s data and modeling infrastructure.
Requirements:
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Applied Mathematics, or a related field.
- 1–5 years of experience building and deploying machine learning models in a production environment, preferably in financial services or high-impact domains.
- Strong Python skills, including use of pandas, NumPy, scikit-learn, and PyTorch or TensorFlow.
- Hands-on experience with LLMs (e.g., GPT, LLaMA, Claude, Falcon), including fine-tuning, inference, prompt design, and embedding techniques.
- Experience working with APIs and unstructured data (e.g., natural language text, PDFs, HTML).
- Familiarity with ML ops concepts (e.g., model versioning, feature stores, deployment tools).
