Job Description: Head of Machine Learning - Natural Language Processing

Our client is a leading quantitative investment fund with a highly sophisticated research and technology platform. As part of the continued expansion of its systematic investment capabilities, we have been retained to identify an exceptional Head of Machine Learning to lead its Natural Language Processing research function.

This is a highly technical, hands-on leadership role for an individual with deep expertise in NLP and Large Language Models (LLMs), who is excited by the opportunity to apply cutting-edge language modelling techniques directly to quantitative investment strategies.

Responsibilities

The successful candidate will lead a team of highly talented researchers and engineers, while remaining actively involved in the research and development of NLP-driven quantitative strategies.

As Head of NLP, you will own the firm's NLP research agenda, combining technical leadership with hands-on research. A significant focus of the role will be the training, fine-tuning, and optimisation of large language models for financial applications. You will investigate how increasingly sophisticated language models can be trained to extract information, identify patterns, and generate differentiated signals from large and complex textual datasets.

Key Responsibilities

• Actively developing NLP-driven quantitative strategies, working directly with quantitative researchers to identify and exploit signals within unstructured data.
• Leading the research and development of large language models, with a particular focus on training, fine-tuning, evaluation, and optimisation.
• Developing bespoke approaches to fine-tune LLMs for financial and investment-specific applications.
• Working with large-scale textual datasets, including news, company filings, earnings transcripts, research, and other alternative data sources.
• Investigating techniques across transformers, representation learning, embeddings, supervised and self-supervised learning, instruction tuning, and retrieval-augmented generation.
• Designing rigorous experiments to evaluate model performance and determine whether NLP-derived outputs translate into robust investment signals.
• Working closely with quantitative researchers to integrate NLP models and signals into systematic investment strategies.
• Staying at the forefront of developments in LLMs and determining which advances have genuine potential to improve the firm's research capabilities.
• Building the infrastructure, research methodologies, and technical standards required to train and evaluate models at scale.
• Managing and developing a team of NLP researchers and engineers, setting the technical direction while remaining deeply involved in the underlying research.