Senior ML Research Engineer - Artificial Intelligence

Senior ML Research Engineer - Artificial Intelligence

Location

London

Business Area

Engineering and CTO

Ref #

Description & Requirements

How do you build machine learning models that can make sense of increasingly complex financial markets — combining structured market data with signals from unstructured and alternative data, while using AI to improve the research process itself?

Bloomberg’s Engineering AI department has 400+ AI practitioners building models and systems that power some of the most important workflows in global financial markets. Our work combines deep expertise in machine learning with Bloomberg’s unique breadth of financial data, news, research and analytics across more than 35 million financial instruments.

We are looking for a Senior ML Research Engineer to join a small, highly technical team focused on financial market modelling. We build models and analytics that support market analysis and financial decision-making, helping Bloomberg clients better understand financial instruments, identify relevant signals and act on complex market information.

Our work spans statistical modelling, machine learning, time series and forecasting, with an increasing focus on combining structured financial data with signals derived from unstructured and alternative data sources.

We work both on the financial models themselves and on AI systems that make the research and modelling process more effective. This includes AI-assisted and iterative research systems that can support hypothesis generation, experimentation, evaluation and model refinement across the modelling lifecycle.

As part of the team, you will work on challenging financial modelling problems end to end — from problem formulation and experimentation through software and system design, deployment, evaluation and continued model improvement. Examples include dividend projection, pricing illiquid instruments, identifying comparable peers, and estimating emissions.

Join us as a Senior ML Research Engineer and you will have the opportunity to:

• Own challenging quantitative ML problems end to end, from research and experimentation through system design, production deployment and ongoing model improvement
• Design, train and evaluate models for financial applications across statistical modelling, machine learning, time series and forecasting
• Develop approaches that combine structured financial data with signals from unstructured and alternative data sources
• Explore AI-assisted and iterative research systems that improve how quantitative models are researched, evaluated and refined
• Collaborate closely with ML researchers, software engineers, product teams and financial domain experts to turn research ideas into production systems
• Write, test and maintain production-quality code and contribute to the design of robust, scalable ML systems
• Provide technical leadership on complex projects and help shape the team’s research and modelling direction
• Stay current with advances in AI, machine learning and quantitative methods, and identify where new approaches can meaningfully improve financial models and products
• Represent Bloomberg through publications, conferences, open-source work and engagement with the broader research and technology community

We are looking for a Senior ML Research Engineer with the following experience:

• Deep practical experience applying machine learning to complex real-world problems, with strong foundations in statistical modelling, classical machine learning and deep learning
• Strong interest in financial markets and quantitative financial problems; prior financial-domain experience is valuable but not required
• PhD in Machine Learning, Computer Science, Statistics, Mathematics or a related quantitative field, or MSc in a relevant field with substantial relevant research and industry experience
• Strong software engineering skills, with experience designing, building and operating robust production systems
• Strong understanding of computer science fundamentals, including algorithms, data structures and data-driven problem solving
• Experience designing rigorous experiments, evaluating models and reasoning carefully about model performance and failure modes
• Excellent communication skills and the ability to collaborate effectively across research, engineering, product and domain teams
• Research depth and impact, demonstrated through publications in leading machine learning, AI, statistics, quantitative methods or related venues
• We give back to the technology community and you can read more about our outreach at:

If indicated, please note that years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role.

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