Machine Learning Engineer Lead
At a glance
Senior Machine Learning Engineering role at RELX. Raleigh · full-time · $115,400–$192,300 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Lead and mentor a team of ML engineers, setting technical direction through code guidance and design reviews
- Design distributed ML platforms for multiple products, including shared infrastructure for language model serving and retrieval
- Build agent frameworks and multi-step reasoning systems, choosing when to use fixed workflows or autonomous agents
- Add safety controls and traceability, then measure reasoning quality, hallucinations and system reliability
- Set ML testing, deployment and CI/CD practices while keeping systems aligned with data governance and responsible AI standards
What you bring
- Eight to ten years building machine learning or software systems, including deploying ML into production
- Two to three years managing people
- A strong software engineering foundation, with experience designing AI products for large user bases and extensive unstructured data
- A bachelor's or master's degree; computer science is strongly preferred
Who this fits
This is a senior, hands-on leadership role that combines engineering work with managing a team of four to five ML engineers. The role is based in Raleigh.
From the employer
About our Team
LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (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
Do you love collaborating with teams to solve complex technical problems?
We are seeking a Machine Learning Engineer Lead to design, build, and operate scalable AI/ML systems and agentic architectures that support next-generation legal research and analytics products. This role combines deep ML expertise with distributed systems engineering and AI platform development.
In this role you will be a hands-on engineer and leader that will lead a high-performing team of 4-5 ML engineers, drive platform-level decisions, and ensure enterprise-grade scalability, reliability, and responsible AI compliance.
Responsibilities:
Lead, mentor, and grow a team of 4-5 ML engineers.
Provide architectural direction and code-level guidance.
Establish engineering best practices for ML system design, testing, and deployment.
Conduct design reviews, performance reviews, and technical roadmap planning.
Architect distributed ML systems serving multiple global products.
Standardize infrastructure patterns for LLM serving and retrieval systems.
Define and implement enterprise-ready agentic frameworks.
Architect multi-step reasoning systems.
Lead decisions on deterministic workflows vs. autonomous agents.
Implement guardrails, safety layers, and traceability mechanisms.
Develop evaluation frameworks to measure reasoning quality, hallucination rates, and reliability.
Establish CI/CD standards for ML lifecycle management.
Ensure compliance with enterprise data governance and responsible AI standards.
Requirements
8-10 years of Machine Learning/Software Engineer experience
2-3 years of people management experience.
Master’s degree or bachelor's degree, computer science degree is highly desirable.
Strong software engineering background with experience in building system design, architecting AI feature/products that caters large number of users and deals with large volume of unstructured data
Experience with ML deployment to production
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