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NEXTMOVEFDE careers · United States
RELX

Machine Learning Engineer Lead

AI summary of the role

Lead a team of 4-5 ML engineers to design, build, and operate scalable AI/ML systems and agentic architectures for next-generation legal research and analytics products.

What you’ll do

  • Lead, mentor, and grow a team of 4-5 ML engineers.
  • Architect distributed ML systems serving multiple global products.
  • Define and implement enterprise-ready agentic frameworks and multi-step reasoning systems.
  • Establish CI/CD standards for ML lifecycle management and ensure compliance with responsible AI standards.

What you’ll bring

  • 8-10 years of Machine Learning/Software Engineer experience.
  • 2-3 years of people management experience.
  • Strong software engineering background with experience in building system design and architecting AI features/products for large user bases and unstructured data.
  • Experience with ML deployment to production.

Technologies

LLM · retrieval systems · agentic frameworks · CI/CD · distributed systems · ML deployment · responsible AI · evaluation frameworks

About RELX

Provides analytics, decision tools, and workflow products for legal, risk, scientific, health, and events customers, built on proprietary data and trusted content.

Public · 5000+ people

Source and classification

Deployment team leadership · Evidence for this classification:

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.
How jobs are selected

Employer postings · Data from · Sources