Machine Learning Specialist - Systematic Trading

Posted 1 day 9 hours ago by Durlston Partners LLP

Permanent
Full Time
Other
London, United Kingdom
Job Description
Machine Learning Specialist - Systematic Trading

AI - ML, Systematic Trading & Research

Upto $300,000 Total Compensation

London, New York

A leading systematic hedge fund, known for its collaborative, research-driven culture, is seeking a Machine Learning Specialist to apply cutting edge ML techniques to enhance trading strategies and infrastructure.

This is a first-of-its-kind role, where you will act as a subject matter expert in ML, working across asset classes and strategies to develop scalable, high impact solutions.

With a highly selective team (predominantly Ivy League/Oxbridge grads) and a multi strat, multi asset approach, this firm offers a unique opportunity to shape the future of machine learning in systematic finance.

Key Responsibilities:

  • Develop & Apply Machine Learning Solutions: Identify opportunities to integrate ML into alpha generation, execution, and risk management.
  • Cross Team Collaboration: Work with quant researchers, traders, and engineers across equities, macro, and volatility strategies.
  • End to End Implementation: Own the ML research pipeline, from feature extraction to production deployment.
  • Stay at the Cutting Edge: Leverage advancements in deep learning, NLP, reinforcement learning, and other AI techniques to enhance systematic trading.
  • Multi Asset Coverage: Apply ML methodologies across equities, vol (rates/converts), commodities, FX, and credit.

Ideal Profile:

  • PhD or Master's in Machine Learning, Computer Science, Applied Mathematics, or a related field, with a strong publication record in top ML/AI conferences.
  • First Principles Thinker: Ability to craft ML solutions tailored to high performance trading environments.
  • Extensive NLP & Feature Engineering Experience: Strong focus on scalable data analysis for signal generation.
  • Strong Programming & ML Stack: Proficiency in Python, TensorFlow, PyTorch, Scikit learn, and distributed computing.
  • Quantitative Finance Exposure: Preference for candidates with quant experience, not just pure AI/ML backgrounds.