Senior Machine Learning Engineer

Job Title: Senior Machine Learning Engineer

Location: Remote (UK)

Salary: Up to £200,000 Plus Equity

This is a unique opportunity for a Senior Machine Learning Engineer to join a high-performing quantitative trading firm building next-generation machine learning systems for financial markets. Working alongside a team of exceptional engineers, researchers, and traders, you will play a key role in developing models and infrastructure that directly influence trading performance and investment decisions.

This is a hands-on role within a fast-moving, technology-led environment where your work will have a measurable impact from day one. While the role is fully remote, candidates must be based in the UK and willing to attend occasional team gatherings and meetings.

The Role

You will design, develop, and deploy advanced machine learning models across a range of quantitative trading and research initiatives. Working closely with trading, data, and engineering teams, you will help build scalable ML systems capable of extracting signals from large and complex datasets while contributing to the long-term technical direction of the platform.

Requirements

• A degree (preferably MSc or PhD) in Mathematics, Statistics, Computer Science, Physics, Engineering, or a related quantitative discipline
• Strong commercial experience developing machine learning models in Python using frameworks such as PyTorch, JAX, or TensorFlow
• Experience building predictive models for time-series, forecasting, signal generation, or large-scale data analysis
• Strong understanding of statistics, probability, optimisation, and quantitative modelling techniques
• Experience working with high-volume datasets and designing scalable research or production pipelines
• Proven ability to take models from research through to live deployment
• A track record of solving complex quantitative problems in fast-paced environments

Desirable Experience

• Experience within quantitative trading, hedge funds, systematic investing, market making, or financial technology
• Knowledge of alpha generation, factor modelling, portfolio optimisation, or execution modelling
• Experience with reinforcement learning, probabilistic modelling, Bayesian methods, or deep learning for financial applications
• Familiarity with market data, alternative datasets, and low-latency or high-performance systems

If this opportunity is of interest, please apply below