ML Engineer

Machine Learning Engineer

A leading quantitative investment firm is seeking a Machine Learning Engineer to join its London-based Data Science team, focused on building advanced data and machine learning solutions for systematic trading. The role involves developing predictive models for time series and financial data, working with large-scale structured and unstructured data sets, and deploying production-grade systems that support investment decision-making.

Working closely with Quant Researchers, Portfolio Managers, and engineering teams, the ML Engineer will translate complex business problems into scalable ML solutions, contributing to the firm's AI and data infrastructure. This position combines strong technical depth with cross-functional collaboration, offering exposure to cutting-edge tools and real-world trading applications.

Key Responsibilities

• Develop machine learning models for time series analysis, forecasting, and financial data applications
• Work with large, unstructured data sets, including market and textual data sources
• Build and maintain scalable data pipelines and ML-driven services
• Write production-level code (Python preferred) and deploy models in containerised environments
• Partner with Quant Researchers, PMs, and Technology teams to translate business problems into data-driven solutions
• Generate insights using advanced statistical and machine learning techniques
• Contribute to the design and implementation of next-generation ML/AI infrastructure
• Communicate technical findings clearly to both technical and non-technical stakeholders
• Manage multiple projects in a fast-paced, collaborative environment

Key Requirements

• Bachelor's degree or higher in Computer Science, Engineering, Operations Research, or a related quantitative field
• 3+ years of hands-on experience in machine learning and statistical modelling on large datasets
• Strong programming skills in Python (preferred), or C++/Scala, with experience in distributed data technologies (e.g., Spark, SQL)
• Experience building and deploying production-grade systems, ideally in containerised environments
• Strong grounding in statistics, optimisation, time series, and scientific computing
• Experience working with financial or alternative data sets (e.g., NLP/text data) is beneficial
• Ability to communicate complex ideas effectively across teams
• Strong ownership mindset and ability to work independently and collaboratively