This opportunity sits within a leading quantitative trading environment at the forefront of applying machine learning to financial markets. The firm operates at scale, leveraging vast datasets and significant computational resources to identify and capture opportunities across highly liquid markets. The culture is defined by speed, autonomy, and an emphasis on measurable impact, where ideas are tested, deployed, and evaluated rapidly.
As a Machine Learning Researcher within the Options team, you will play a critical role in developing advanced models that directly influence trading performance. You will take full ownership of the research lifecycle, from idea generation through to production deployment, working on complex, high-dimensional problems where success is clearly measured by real-world outcomes.

Role Overview

You will focus on designing, building, and deploying cutting-edge machine learning models in a fast-paced environment. This role combines deep technical research with practical implementation, requiring both strong theoretical foundations and the ability to translate ideas into production systems that drive P&L.
Operating within a flat, non-bureaucratic structure, you will have the autonomy to pursue high-impact research directions, iterate rapidly, and deploy solutions at scale with direct visibility into performance.

Key Responsibilities

• Own the full research lifecycle, including hypothesis generation, experiment design, model validation, risk controls, and production deployment
• Conduct advanced research in machine learning and AI, applying state-of-the-art techniques (e.g. deep learning, transformers, representation learning) to quantitative finance problems
• Develop and deploy models that have a measurable impact on trading performance in options markets
• Rapidly prototype, test, and iterate on ideas, moving efficiently from concept to production
• Identify alpha opportunities within large, complex, and high-dimensional datasets
• Build scalable research pipelines, including feature engineering, distributed training, and backtesting systems
• Collaborate closely with trading and engineering teams to translate research insights into executable strategies
• Leverage extensive compute and data resources to run ambitious experiments at scale
• Continuously refine models and strategies based on live performance and evolving market conditions

Skills and Experience

• Advanced academic background (Master's or PhD) in Mathematics, Statistics, Physics, Computer Science, or a related quantitative discipline
• Strong research experience in machine learning, AI, or statistical modelling, with evidence of solving complex, real-world problems
• Deep understanding of modern machine learning architectures and training methodologies, including large-scale models
• Experience with training techniques such as pre-training, fine-tuning, reinforcement learning, and optimisation methods
• Proven ability to take models from concept through to production with measurable impact
• Strong mathematical foundations, including linear algebra, probability, and optimisation
• Fluency in Python and experience with relevant libraries (e.g. NumPy, PyTorch), with the ability to write clean, scalable code
• Hands-on experience with modern ML approaches such as sequence models, transformers, regularisation techniques, and robust validation methods
• Experience working with large-scale datasets and distributed systems
• A practical, results-oriented mindset with a bias for action and rapid iteration

Personal Attributes

• Highly curious, with a strong interest in financial markets and a desire to build domain expertise in options and market microstructure
• Comfortable operating in a fast-paced, high-performance environment with significant ownership and autonomy
• Strong problem-solving skills and the ability to simplify complex challenges into actionable solutions
• Collaborative mindset, with the ability to engage effectively with both technical and non-technical stakeholders
• Motivated by impact, with a clear focus on delivering measurable results

What Sets This Opportunity Apart

• Direct link between your work and trading outcomes, with clear feedback loops on performance
• Access to large-scale data and significant computational resources
• A flat structure that encourages ownership, speed, and entrepreneurial thinking
• The ability to work on cutting-edge machine learning problems with real-world financial impact
• A highly collaborative and intellectually rigorous environment