ML Quant Researcher
Core
Design, develop, and deploy LLM-driven pipelines to extract alpha signals from unstructured financial data (news, filings, transcripts) and build end-to-end ML systems for systematic trading.
Role type
Machine Learning / NLP Research Engineer (Systematic Trading)
Builds
LLM-driven signal extraction pipelines, end-to-end ML systems, and alpha generation strategies
Domain
Finance / Quantitative Trading / Natural Language Processing
Deliverable
production ML models
Required skills
Python, PyTorch, TensorFlow, Hugging Face, LangChain, RAG architectures, vector databases, statistical learning, experimental design, data engineering, SQL, time-series data handling
Preferred skills
NLP/ML in finance, alpha research, systematic trading, time-series modelling, cloud/distributed compute
Technologies
PyTorch, TensorFlow, Hugging Face, LangChain, vector databases
Responsibilities
Design and deploy LLM pipelines for unstructured financial data; Build and scale ML systems from research to production; Apply NLP and retrieval techniques to large text datasets; Develop innovative alpha signals; Implement backtesting, validation, and monitoring frameworks; Evaluate model performance and data quality; Optimise data pipelines for speed and scalability
Seniority
Mid-level, hands-on IC
