ML Quant Researcher

Detailed Job Requirements

We are seeking a highly motivated Machine Learning / NLP Research Engineer to join a leading systematic trading environment, focused on applying cutting-edge LLM and NLP techniques to financial data and alpha generation.

Core Responsibilities

• Design, develop, and deploy LLM-driven pipelines for extracting signals from unstructured financial data (e.g. news, filings, earnings transcripts, research reports)
• Build and scale end-to-end ML systems, from research and experimentation through to production deployment
• Apply NLP and retrieval techniques (e.g. RAG architectures, embeddings, semantic search) to structure and leverage large text datasets
• Develop innovative alpha signals using both structured and unstructured data sources
• Collaborate closely with researchers, traders, and engineers to translate research ideas into production systems
• Implement robust backtesting, validation, and monitoring frameworks to ensure signal integrity and performance
• Continuously evaluate model performance, data quality, and potential sources of bias, overfitting, and data leakage
• Optimise data pipelines for speed, scalability, and reliability in a high-performance computing environment

Required Skills & Experience

• 2-5 years of experience in machine learning, NLP, or applied AI within a high-performance or data-intensive environment (finance experience beneficial but not essential)
• Strong Python programming skills, with the ability to write clean, scalable, production-quality code
• Hands-on experience with modern ML/NLP frameworks (e.g. PyTorch, TensorFlow, Hugging Face, LangChain)
• Experience building or working with LLMs, including prompt engineering, evaluation, and fine-tuning
• Familiarity with retrieval systems (vector databases, embeddings, RAG pipelines)
• Strong understanding of statistical learning, modelling, and experimental design
• Experience handling large, complex datasets (structured and unstructured)
• Solid knowledge of data engineering concepts, including pipelines, distributed processing, and SQL/time-series data

Desirable Experience

• Experience applying NLP/ML techniques to financial or alternative data
• Knowledge of alpha research, systematic trading, or quantitative investment strategies
• Experience with time-series modelling and signal generation
• Familiarity with cloud or distributed compute environments
• Advanced degree (MSc/PhD) in a quantitative discipline (e.g. Computer Science, Mathematics, Physics, Statistics, Engineering)

Candidate Profile

• Strong problem-solving ability with a research-driven, hypothesis-led mindset
• Ability to work independently in a fast-paced, high-impact environment
• Intellectual curiosity and interest in both machine learning and financial markets
• Strong communication skills, with the ability to translate complex ideas into practical solutions

This is an opportunity to operate at the intersection of machine learning and financial markets, building next-generation NLP-driven alpha strategies in a highly collaborative and performance-focused environment.