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# Senior ML Quant Engineer - Fixed Income - Artificial Intelligence

- **Company:** Bloomberg
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Structures, Machine Learning, Software Engineering, Information Technology
- **Published:** May 19, 2026
- **Apply:** https://dejobs.org/x/x/C8E6565AEC674E86A6FC003AD7C1A59F/job/

## About the Role

* Previous relevant work experience with Machine Learning or Statistical Modeling techniques in the financial industry, ideally around asset valuation. A track record designing, building, evaluating, and maintaining statistical or Machine Learning solutions in production is a plus.
 * Ph.D. or M.Sc. with equivalent research experience in Machine Learning, Computer Science, Mathematics, Statistics or a related field.
 * Thriving in solving challenging, often ill-defined problems where off-the-shelf solutions fall short, and bring a creative, rigorous approach to developing novel methods and technologies.
 * Proficiency in software engineering with an understanding of Computer Science fundamentals such as data structures and algorithms.
 * Excellent communication skills and the ability to collaborate with engineering peers as well as non-engineering stakeholders.
 * A track record of authoring publications in top conferences and journals is a strong plus.

## Description

* Design, build and evaluate statistical and Machine Learning models that directly influence how global markets price fixed income assets
 * Collaborate with cross-functional teams to develop, test, monitor and maintain robust production systems.
 * Design new architectures, systems and tools to power next-generation pricing capabilities of Bloomberg.
 * Integrate cutting-edge academic and industry research into models and methodologies, staying ahead of emerging developments to drive continuous innovation.
 * Represent Bloomberg at scientific and industry conferences, and publish research findings through documentation, whitepapers, or in leading academic journals and conferences.


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