ML Engineering Lead: Scalable AI Platforms

RELX Inc. Company; North Carolina; On-site; USD 115,000 - 192,000; Full time

Benefits offered by this job

Annual incentive bonus

Country-specific benefits

Job summary

RELX Inc. is seeking a Machine Learning Engineer Lead to design, build, and operate scalable AI/ML systems for legal research products. You will lead a team of 4-5 engineers, influence architecture and drive enterprise-grade reliability and responsible AI practices.

You will guide platform-level decisions, standardize ML workflows, and partner with product and data teams to deliver impact at scale while maintaining compliance and data governance.

Qualifications

• 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.
• Experience with ML deployment to production.

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.
• Architect distributed ML systems serving multiple global products.
• 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.

Skills

Team leadership

ML deployment

Distributed systems

Python

AI/ML systems

Education

Master’s degree

Bachelor’s degree

Tools

Git

Docker

Kubernetes

TensorFlow

PyTorch

Job description

RELX Inc. is seeking a Machine Learning Engineer Lead to design, build, and operate scalable AI/ML systems for legal research products. You will lead a team of 4-5 engineers, influence architecture and drive enterprise-grade reliability and responsible AI practices.

You will guide platform-level decisions, standardize ML workflows, and partner with product and data teams to deliver impact at scale while maintaining compliance and data governance.