Senior Machine Learning Engineer III

Would you like to apply advanced machine learning techniques to uncover meaningful patterns from complex data?
Are you motivated by building practical, high‑impact predictive models used in real‑world applications?

About our Team
LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (http://www.relx.com), 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 (https://stories.relx.com/responsible-ai-principles/index.html).

About the Role
In this role, you will design, study, and train machine learning algorithms that learn from complex, high‑dimensional data. You will focus on transforming patterns discovered in data into practical, usable predictive models and applications. Your work will contribute directly to advancing natural language processing and machine perception capabilities.

Responsibilities

• Construct and train machine learning algorithms using complex, high‑dimensional datasets
• Study data patterns to develop practical predictive models and applications
• Apply techniques such as random forests, deep learning, and generative modeling
• Improve the predictive performance of natural language processing algorithms
• Enhance machine perception models through experimentation and iteration
• Evaluate and refine models to support real‑world use cases

Requirements

• Experience developing machine learning algorithms for complex data
• Knowledge of high‑dimensional data analysis and modeling
• Hands‑on experience with techniques such as random forests and deep learning
• Familiarity with generative modeling approaches
• Understanding of neural network architectures and memory concepts
• Ability to translate analytical findings into usable models and applications

Work in a Way That Works for You
We promote a healthy work/life balance across the organisation. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long‑term goals.

Working Pattern
Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.

Working for You
We know that your well‑being and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:

• Private Medical/Dental Plan
• Savings Fund
• Life Insurance
• Meal/Grocery Voucher

About the Business
LexisNexis 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.

We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.

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We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.

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