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 AI Engineers with expertise and a passion for Information Retrieval, Search technologies, Natural Language Processing and Generative AI to join our AI Experiences team. Our teams are working on exciting initiatives such as: Developing and deploying robust Retrieval-Augmented Generation (RAG) systems, curating high-quality data for model training and evaluation, and building evaluation frameworks to enable rapid iteration and continuous improvement based on real-world user interactions. Designing and implementing tools that enable LLM-powered search agents to effectively handle complex client queries, shaping Bloomberg's generative AI ecosystem, and scaling these innovative solutions to support thousands of users. Leveraging both traditional ML approaches and Generative AI to prototype, build, and maintain high-performing, client-facing search and streaming applications that deliver timely and relevant financial insights. Building robust APIs to facilitate search across diverse collections of data, ensuring highly relevant results and maintaining system stability and reliability. You'll have the opportunity to: Collaborate closely with cross-functional teams, including product managers and engineers, to integrate AI solutions into client facing products, enhance analytical capabilities and improve user experience. Architect, develop, and deploy production-quality search systems powered by LLMs, emphasizing both ML innovation and solid software engineering practices. Continuously identify areas for improvement within our search systems, proactively experiment with new ideas, and rapidly implement promising solutions-even when improvements rely purely on engineering without direct ML involvement. Design, train, test, and iterate on models and algorithms while taking ownership of the entire lifecycle, from idea inception to robust deployment and operationalization. Stay at the forefront of research in IR, NLP, and Generative AI, incorporating relevant innovations into practical, impactful solutions. Represent Bloomberg at industry events, scientific conferences, and within open-source communities. You'll need to have: Proven practical experience solving real-world problems using NLP, Information Retrieval, Search technologies, and Generative AI technologies. Knowledge of search platforms like Apache Solr or Elasticsearch is a plus. Significant industry experience or a relevant academic background (such as an MSc or PhD in Computer Science, Machine Learning, Mathematics, Statistics, or Engineering). We value practical ability and a demonstrated track record of delivering impactful results over formal academic qualifications. Proficiency in enterprise-grade software engineering practices and principles, including solid fundamentals in data structures, algorithms, and a data-driven problem-solving approach. Solid communication skills, with the ability to collaborate effectively with both technical peers and business stakeholders to ensure alignment between technical solutions and client needs.