Senior Research Engineer - Information Retrieval - Artificial Intelligence

Description & Requirements

Bloomberg's Engineering AI department has 350+ AI practitioners building highly sought after products and features that often require novel innovations. We are investing in AI to build better search, discovery, and workflow solutions using technologies such as transformers, gradient boosted decision trees, large language models, and dense vector databases. We are expanding our group and seeking highly skilled individuals who will be responsible for contributing to the team (or teams) of Machine Learning (ML) and Software Engineers that are bringing innovative solutions to AI-driven customer-facing products.

At Bloomberg, we believe in fostering a transparent and efficient financial marketplace. Our business is built on technology that makes news, research, financial data, and analytics on over 35 million financial instruments searchable, discoverable, and actionable across the global capital markets.

Bloomberg has been building Artificial Intelligence applications that offer solutions to these problems with high accuracy and low latency since 2009. We build AI systems to help process and organize the ever-increasing volume of structured and unstructured information needed to make informed decisions. Our use of AI uncovers signals, helps us produce analytics about financial instruments in all asset classes, and delivers clarity when our clients need it most.

We are looking for Senior Research Engineers with expertise and a passion for Information Retrieval, Natural Language Processing and Generative AI to join our AI Experiences team. Our teams are working on exciting initiatives such as:

• Building and deploying RAG systems, curating data for model training and evaluation, building evaluation systems that facilitate rapid iteration, and understanding how users interact with our systems to identify directions for improvements.
• Designing tools that Search LLM agents can access to respond to clients query, contributing to shaping and implementing the LLM agentic ecosystem in Bloomberg and scaling generative AI applications to thousands of users.
• Applying traditional machine learning, NLP and GenerativeAI to prototype and productionize client-facing search and streaming applications for discovering the most relevant and timely financial news.

You'll have the opportunity to:

• Collaborate with colleagues on building and deploying production-grade search systems powered by LLMs using sound ML and software engineering practices.
• Continuously identify opportunities to improve our search systems and rapidly explore and experiment ideas for improvement, and release ones that have promise.
• Design, train, experiment, and evaluate models, algorithms and solutions.
• Anticipate data needs for building and evaluating ML-driven systems. Take ownership of securing such data by coordinating with data owners and annotators. At the same time, identify opportunities for using LLMs in dealing with data scarcity issues.
• Demonstrate technical leadership by owning cross-team projects.
• Stay current with the latest research in IR, NLP, LLMs and incorporate new findings into our models and methodologies.
• Represent Bloomberg at scientific and industry conference and in open-source communities.
• Publish product and research findings in documentation, whitepapers or publications to leading academic venues.

You'll need to have:

• Practical experience with Natural Language Processing or Information Retrieval problems.
• A Ph.D. in NLP, IR, ML, or a relevant field or an MSc in CS, ML, Math, Statistics, Engineering with prior industry experience. We are happy to consider candidates with a Ph.D and industry experience through internships.
• Experience with deep learning frameworks such as PyTorch.
• Proficiency in software engineering.
• An understanding of Computer Science and software engineering fundamentals such as data structures and algorithms and a data-oriented approach to problem-solving.
• 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.

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