Machine Learning Quant Engineer - Investment banking

Overview

Senior Quant Machine Learning Engineer sought by leading investment bank based in the city of London.

Inside IR35, 4 days a week on site

The role:

To lead the design and deployment of ML-driven models across our trading and investment platforms. This is a high-impact, front-office role offering direct collaboration with traders, quant researchers, and technologists at the forefront of financial innovation.

Your Role

• Design, build, and deploy state-of-the-art ML models for alpha generation, portfolio construction, pricing, and risk management
• Lead ML research initiatives and contribute to long-term modeling strategy across asset classes
• Architect robust data pipelines and scalable model infrastructure for production deployment
• Mentor junior quants and engineers; contribute to knowledge-sharing and model governance processes
• Stay current with cutting-edge ML research (e.g., deep learning, generative models, reinforcement learning) and assess applicability to financial markets
• Collaborate closely with cross-functional teams, including traders, data engineers, and software developers

What We're Looking For

Required:

• 7+ years of experience in a quant/ML engineering or research role within a financial institution, hedge fund, or tech firm
• Advanced degree (PhD or Master's) in Computer Science, Mathematics, Physics, Engineering, or related discipline
• Strong expertise in modern ML techniques: time-series forecasting, deep learning, ensemble methods, NLP, or RL
• Expert-level programming skills in Python and strong understanding of software engineering best practices
• Experience deploying ML models to production in real-time or high-frequency environments
• Deep understanding of financial markets and quantitative modeling

Preferred:

• Experience in front-office roles or collaboration with trading desks
• Familiarity with financial instruments across asset classes (equities, FX, fixed income, derivatives)
• Experience with distributed computing frameworks (e.g., Spark, Dask) and cloud-native ML pipelines
• Exposure to LLMs, graph learning, or other advanced AI methods
• Strong publication record or open-source contributions in ML or quantitative finance

Please apply within for further details or call on

Alex Reeder

Harvey Nash Finance & Banking