Bloomberg Law/Tax/Gov is transforming the legal industry through advanced AI-powered research and analytics solutions. Our platform combines sophisticated search, retrieval, and generative AI capabilities to provide fast, reliable access to legal content, company information, analytics, and real-time answers. We are focused on redefining how professionals conduct research and make decisions by building intelligent systems that automate workflows, surface actionable insights, and deliver trusted answers grounded in authoritative content.

As large language models and Retrieval-Augmented Generation (RAG) technologies reshape enterprise search and knowledge discovery, we are investing in next-generation AI systems that combine state-of-the-art machine learning with scalable information retrieval infrastructure. Our goal is to build best-in-class AI experiences that are accurate, explainable, performant, and deeply integrated into the workflows of our clients.

Who are we?

Bloomberg Law/Tax/Gov’s Search AI Team is a group of Machine Learning Engineers passionate about solving complex problems in the legal/tax domains using cutting-edge AI technologies. Our team develops large-scale machine learning and retrieval systems leveraging Natural Language Processing (NLP), Natural Language Understanding (NLU), Information Retrieval (IR), and Retrieval-Augmented Generation (RAG) techniques. We work on problems including semantic search, entity resolution, ranking and recommendation systems, query understanding, document understanding, and grounded generative AI experiences.

We work in an agile environment, partnering closely with our product teams, software and ML engineering teams, and legal domain experts to rapidly design, build, and deploy AI-powered solutions at scale.

We’ll trust you to:

• Advance our query understanding framework by improving query parsing, entity linking, and named entity recognition to deliver more accurate and context-aware retrieval experiences

• Design and optimize end-to-end Retrieval-Augmented Generation (RAG) pipelines, including ingestion, indexing, retrieval, re-ranking, and LLM-based answer generation

• Personalize search and generative AI experiences by analyzing user workflows and adapting retrieval and ranking strategies to individual user needs

• Improve information and content discoverability by building semantic, ranking, and retrieval models that surface relevant and trustworthy content

• Design and implement evaluation frameworks that leverage implicit and explicit user feedback to continuously measure and improve retrieval quality, answer relevance, and overall user experience

• Apply state-of-the-art NLP, NLU, Information Retrieval, and generative AI techniques to deliver grounded, accurate, and explainable answers from large and complex legal content sets

• Architect and develop scalable backend services and APIs that support high-throughput, low-latency AI and retrieval workloads across distributed systems

• Operate and optimize scalable systems for handling search queries while meeting stringent SLAs for latency, availability, and reliability

• Collaborate closely with product managers, software engineers, and domain experts to bring AI-powered capabilities into production

• Design, write, test, and maintain modular, production-quality code while contributing to engineering best practices across the team

You’ll need to have:

• 4+ years of industry experience developing and deploying machine learning systems in production environments

• Strong understanding of machine learning fundamentals, information retrieval concepts, and modern NLP techniques

• Working knowledge of common ML frameworks such as PyTorch, Tensorflow, Scikit-learn and willingness to learn new technologies as needed

• Familiarity with vector search technologies and search platforms such as Solr/OpenSearch, FAISS, Pinecone, or similar technologies

• Strong programming skills in Python, Java, or similar languages

• Experience working with large language models and embedding-based retrieval systems

• Experience designing scalable backend systems and APIs in distributed environments

• Familiarity with cloud infrastructure and modern engineering practices, including AWS, Docker, Kubernetes, and CI/CD workflows

• Curiosity to solve complex technical problems and continuously learn emerging AI technologies

• Passion for building intelligent systems that deliver measurable impact to end users

We’d love to see:

• Prior experience working on enterprise search, knowledge discovery, or question-answering systems

• Experience evaluating and improving RAG systems using ranking metrics, user feedback, or automated techniques

• Experience optimizing ML systems for latency, throughput, and scalability

• Familiarity with recommendation systems, ranking models, or personalization frameworks

• Exposure to legal, financial, or other complex domain-specific content ecosystems

Bloomberg is an equal opportunities employer, and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.