As a Senior Machine Learning Engineer, you’ll play a key role in building and scaling Axi’s data-driven trading capabilities. You’ll focus on developing machine learning models and quantitative frameworks using large-scale datasets, helping uncover insights that drive smarter trading and risk decisions.

This is a hands-on technical role at the intersection of machine learning, data engineering, and quantitative research — ideal for someone who enjoys working with complex datasets and translating them into real-world impact.

What you’ll do

• Design and build machine learning models using large-scale datasets (millions to billions of rows), including time-series and event-driven data
• Analyse client behaviour, flow data, and market signals to uncover actionable insights
• Develop data-driven approaches to support trading strategies, risk optimisation, and execution efficiency
• Work with structured and unstructured datasets to identify patterns, anomalies, and predictive signals
• Collaborate with trading, product, and engineering teams to translate models into production-ready solutions
• Evaluate model performance using historical and real-time data, ensuring robustness and scalability
• Continuously improve modelling approaches, feature engineering, and data pipelines
• Contribute to building a scalable quantitative and machine learning ecosystem within the firm

What we’re looking for

• Strong experience in machine learning / data science, particularly working with large and complex datasets
• Proven experience building models using time-series data or high-frequency/event-driven datasets
• Solid foundation in statistics, predictive modelling, and data analysis
• Strong hands-on programming skills in Python
• Experience working with data at scale (e.g. distributed systems, large databases, or streaming data)
• Ability to work independently, prioritise effectively, and deliver high-quality technical solutions

Nice to have (but not required)

• Exposure to trading, financial markets, or quantitative finance concepts
• Experience working with client behaviour / segmentation / transaction data
• Familiarity with kdb+ / KDB or other time-series databases
• Understanding of systematic trading or execution strategies