Senior Machine Learning Engineer

About the Business

Lexis Nexis Legal & Professional® provides legal, regulatory, and business information and analytics that help customers increase their productivity, improve decision‑making, achieve better outcomes, and advance the rule of law around the world. As a digital pioneer, the company was the first to bring legal and business information online with its Lexis® and Nexis® services.

Lexis Nexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (), a global provider of information‑based analytics and decision tools for professional and business customers. Our company has been a long‑time leader in deploying AI and advanced technologies to the legal market to improve productivity and transform the overall business and practice of law, deploying ethical and powerful generative AI solutions with a flexible, multi‑model approach that prioritizes using the best model from today’s top model creators for each individual legal use case.

The company employs over 2,000 technologists, data scientists, and experts to develop, test, and validate solutions in line with RELX Responsible AI Principles (‑ai‑principles/index.html).

This position performs advanced machine learning research, design, and development assignments within a defined functional area or product line. The role is responsible for designing, implementing, training, evaluating, and deploying machine learning models and supporting data pipelines in production environments. This position contributes directly to technical design decisions, development methodologies, and project execution, and collaborates with cross‑functional teams to translate business and product requirements into scalable machine learning solutions.

The role may provide technical guidance to less‑experienced machine learning engineers and actively contribute to improving team ML practices.

Conditions of Employment – Ability to work a hybrid schedule reporting to Raleigh, NC Office location.

• 4+ years of experience in Machine Learning Engineering, Data Science, or Software Engineering with significant ML responsibilities
• BS in Computer Science, Engineering, Mathematics, Statistics, or a related field required; MS or equivalent experience preferred

• Strong knowledge of machine learning algorithms and techniques, including supervised and unsupervised learning, model evaluation, and optimization
• Strong proficiency in one or more ML development languages (e.g., Python, Java, Scala)
• Hands‑on experience with machine learning frameworks and libraries (e.g., PyTorch, Tensor Flow, scikit‑learn)
• Experience designing and maintaining end‑to‑end ML pipelines, including data ingestion, feature engineering, training, validation, and deployment
• Solid understanding of data modeling principles and experience working with structured and unstructured data
• Strong proficiency with SQL and experience working with large‑scale data storage systems
• Experience deploying and monitoring ML models in production environments
• Familiarity with software development best practices, including code reviews, testing, and CI/CD
• Working knowledge of Agile development methodologies
• Ability to research, evaluate, and apply new machine learning techniques and tools
• Ability to diagnose and resolve complex issues related to model performance, data quality, and system integration
• Strong oral and written communication skills

• Design, develop, and maintain production‑grade machine learning models and supporting pipelines
• Collaborate with product managers, software engineers, and data engineers to define and refine ML requirements and solutions
• Translate complex business problems into well‑defined machine learning tasks and system designs
• Contribute to technical design documents and review designs for ML components and systems
• Implement coding best practices, testing strategies, and code reviews for ML development
• Debug, optimize, and resolve complex issues across the ML lifecycle, including data, models, and deployment
• Monitor model performance and support ongoing model improvements and…