R

RELX

Global information analytics and decision tools

Machine Learning Engineering Lead

Full-TimeUpdated on 10/2/2026
No salary listed
Mid
London, UK
In Person

About the job

Requirements
  • Significant hands-on experience in machine learning engineering, software engineering, data engineering, or a related technical discipline.
  • Experience designing, building, deploying, and operating machine learning, artificial intelligence, large language model, or data-driven systems in production.
  • Experience integrating artificial intelligence and machine learning services with enterprise systems, application programming interfaces, databases, data platforms, legacy applications, or internal services.
  • Experience working with cross-functional teams to understand business processes, data flows, content repositories, integration points, and operational constraints.
  • Experience working with Amazon Web Services or cloud-hosted production environments.
  • Equivalent technical experience or education is considered.
  • Strong Python development skills for machine learning engineering, data processing, automation, service development, and production artificial intelligence and machine learning workflows.
  • Strong software engineering background, including system design, application programming interfaces, distributed systems, automated testing, code review, maintainability, reliability, and production support.
  • Strong understanding of machine learning engineering and machine learning operations practices, including model lifecycle management, continuous integration and continuous delivery, testing, monitoring, release management, observability, and operational support.
  • Practical experience with large language model capabilities, including retrieval-augmented generation, semantic search, embeddings, prompt design, evaluation, guardrails, and observability.
  • Experience with agentic workflows, tool orchestration, and multi-step artificial intelligence processes.
  • Strong Amazon Web Services knowledge, including cloud-hosted applications, data services, security controls, logging, monitoring, and production support.
  • Strong understanding of software development life cycle practices, including requirements analysis, design, implementation, automated testing, code review, secure coding, deployment, and production support.
  • Strong understanding of responsible artificial intelligence practices, including evaluation, traceability, secure data handling, model governance, human oversight, and risk management.
  • Ability to work with structured, semi-structured, and unstructured data sources.
  • Ability to understand legacy systems, domain processes, data flows, and integration constraints.
  • Practical experience using artificial intelligence-assisted development tools such as GitHub Copilot, Codex, Claude, or similar tools to improve software delivery.
  • Strong problem-solving skills, including identifying, researching, troubleshooting, and resolving complex technical, data, and integration issues.
  • Strong communication and technical writing skills, including the ability to explain machine learning and engineering concepts clearly to technical and non-technical stakeholders.
Responsibilities
  • Serve as the initial point of escalation for artificial intelligence and machine learning engineering issues within the area of responsibility.
  • Interface with software engineers, data engineers, product stakeholders, domain experts, platform teams, and other technical personnel to finalise requirements and clarify integration needs.
  • Write and review portions of detailed specifications for the development of complex artificial intelligence and machine learning, large language model, retrieval-augmented generation, and agentic workflow components.
  • Design, build, integrate, deploy, and operate production artificial intelligence and machine learning and large language model-based services for legal research, analytics, and content use cases.
  • Implement retrieval-augmented generation, semantic search, embeddings-based retrieval, ranking, summarisation, classification, content enrichment, and citation-aware artificial intelligence capabilities where appropriate.
  • Design and implement agentic workflows, tool orchestration, and multi-step artificial intelligence processes that are reliable, traceable, and governed.
  • Integrate artificial intelligence and machine learning capabilities with enterprise systems, application programming interfaces, databases, data platforms, content repositories, legacy applications, internal services, and Amazon Web Services-hosted services.
  • Establish evaluation and quality controls for accuracy, groundedness, citation quality, hallucination risk, agent task success, latency, cost, reliability, and business value.
  • Implement development processes, coding best practices, code reviews, machine learning operations practices, and responsible artificial intelligence controls.
  • Apply artificial intelligence-assisted development tools to reduce software development cycle time and support code explanation, test generation, refactoring, debugging, documentation, code review, migration planning, and legacy system analysis.
  • Resolve complex technical issues related to artificial intelligence and machine learning services, data flows, system integration, model behaviour, production support, and operational reliability.
  • Mentor and/or train engineers as directed by department management, ensuring they are knowledgeable in critical aspects of artificial intelligence and machine learning engineering, machine learning operations, software development life cycle practices, and responsible use of artificial intelligence-assisted development tools.
  • Keep abreast of relevant technology developments in machine learning engineering, large language models, agentic workflows, Amazon Web Services cloud services, responsible artificial intelligence, and software engineering practices.
  • Ensure artificial intelligence and machine learning solutions align with enterprise data governance, security, privacy, responsible artificial intelligence, and operational standards.
  • Perform all other duties as assigned.
Desired Qualifications
  • Experience with Docker, Kubernetes, Amazon Web Services Elastic Kubernetes Service or Elastic Container Service, Terraform, or similar cloud deployment technologies.
  • Working knowledge of C#/.NET and SQL Server, particularly for integration with enterprise or legacy systems.
  • Experience with event-driven architecture, messaging, queues, asynchronous processing, retries, idempotency, and failure handling.
  • Experience with legal content systems, LegalTech, publishing platforms, case law, citation systems, legal research workflows, XML/XSLT, structured content processing, search, ranking, indexing pipelines, or content enrichment.

About the company

RELX is a global provider of information-based analytics and decision tools for professionals across scientific, technical, medical, legal, risk, business, and exhibitions industries. It operates in four segments: Scientific, Technical & Medical; Risk & Business Analytics; Legal; and Exhibitions. Its products combine large datasets, analytics, and decision-support tools to help researchers, healthcare professionals, lawyers, and business leaders make informed choices, improve productivity, and achieve better outcomes. Revenue mainly comes from subscriptions, with additional income from transactional sales and advertising. RELX differentiates itself through its deep data assets, specialized industry knowledge, and multi-segment offerings that span content, analytics, and decision-support services. The company pursues a goal of delivering trusted information and insights while supporting corporate responsibility and alignment with the UN SDGs through its products and partnerships.

Company Size

1,001-5,000

Company Stage

IPO

Headquarters

London, United Kingdom

Founded

1894

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Simplify Jobs

Simplify's Take

What believers are saying

  • H1 2026 revenue grew 7%; Legal grew 10%; Risk grew 8%.
  • February 24, 2026 Lexis+ with Protégé launched globally, expanding AI workflow adoption.
  • September 3, 2026 Doctrine closes, targeting one million European legal professionals.

What critics are saying

  • Anthropic and Thomson Reuters compressed legal AI pricing after February 2026 market shock.
  • Harvey and Legora court law schools in 2026, training future buyer cohorts elsewhere.
  • Exhibitions face Middle East event uncertainty through 2026, threatening margins and calendar-dependent cash flows.

What makes RELX unique

  • LexisNexis’ 200 billion-document corpus still beats generic legal AI models.
  • September 3, 2026 Doctrine acquisition deepens RELX’s European legal AI workflow moat.
  • H1 2026 subscriptions supplied 62% of revenue, stabilizing cash generation.

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Benefits

Health Insurance

Dental Insurance

Vision Insurance

Life Insurance

Disability Insurance

401(k) Retirement Plan

401(k) Company Match

Wellness Program

Mental Health Support

Family Planning Benefits

Health Savings Account/Flexible Spending Account

Flexible Work Hours

Remote Work Options

Company News

Citybiz
Apr 28th, 2026
RELX to Acquire Legaltech Platform Doctrine in European AI Push

RELX Group has entered into an agreement to acquire French legal technology company Doctrine, a move that would expand its... Read More

Business Insider
Mar 1st, 2026
LexisNexis CEO dismisses AI threat as company reports 7% revenue growth

LexisNexis CEO Sean Fitzpatrick has dismissed investor concerns that AI threatens the legal software giant, saying proprietary data gives it a competitive advantage. Shares of parent company Relx fell 14% on 3 February following Anthropic's launch of an AI agent that drafts legal briefs, and are down 17% year-to-date. Fitzpatrick said LexisNexis's database of 200 billion legal documents, including content from Shepard's Citations dating to 1873, cannot be replicated by AI model makers. The company doesn't license its proprietary data to general-purpose AI providers. Relx reported 7% revenue growth for 2025 and 9% increase in adjusted operating profit, driven by customers adopting AI tools. LexisNexis is hiring and hasn't conducted AI-related redundancies.

Yahoo Finance
Feb 22nd, 2026
RELX's LexisNexis launches AI-powered identity management platform for healthcare

LexisNexis Risk Solutions, a wholly owned subsidiary of RELX, has launched a new identity management platform for the healthcare sector. Announced on 19 February, the tool combines identity verification, matching and enrichment capabilities with AI-powered authentication to enhance security throughout patients' healthcare journeys. The platform integrates LexisNexis IDVerse and offers enhanced identity verification, resolution and fraud protection. For healthcare professionals, it accelerates onboarding processes, automates processing and provides digital checks to prevent fraud. RELX is a global information and analytics company serving professional and business customers across scientific, technical, medical, legal and risk management markets.

PR Newswire
Jun 4th, 2025
Elsevier Unveils Rigorous Evaluation Framework To Mitigate Risk In Generative Ai Clinical Decision Support Tools

Clinician-centered framework will be featured in upcoming issue of the Open AccessJournal of the American Medical Informatics Association (JAMIA)Initial evaluations of ClinicalKey AI show high accuracy and usefulness in responses amongst cliniciansNEW YORK, June 4, 2025 /PRNewswire/ -- Elsevier, a global leader in medical information and data analytics, unveiled a groundbreaking evaluation framework for assessing the performance and safety of generative AI-powered clinical reference tools. This innovative approach has been developed for all Elsevier Health generative AI solutions, including ClinicalKey AI, Elsevier's advanced clinical decision support platform, and sets a new standard for responsible AI integration in healthcare. It will be featured in a future issue of the Open Access Journal of the American Medical Informatics Association (JAMIA).The framework, designed with input from clinical subject matter experts across multiple specialties, evaluates AI-generated responses along five critical dimensions: query comprehension, response helpfulness, correctness, completeness, and potential for clinical harm. It serves as a comprehensive assessment to ensure that AI-powered tools not only provide accurate and relevant information but also align with the practical and current needs of healthcare professionals at the point of care.Omry Bigger, President of Clinical Solutions at Elsevier: "This evaluation framework not only supports innovation and advancements to improve patient care but adds an extra layer of review and assessment to ensure physicians are armed with the most accurate information possible. It's a critical step in the implementation of responsible AI for healthcare providers and patients."In a recent evaluation study of ClinicalKey AI, Elsevier worked with a panel of 41-board certified physicians and clinical pharmacists to rigorously test responses generated by the tool for a diverse set of clinical queries. That panel evaluated 426 query-response pairs, and results demonstrated impressive performance, with 94.4% of responses rated as helpful, 95.5% assessed as completely correct, with just 0.47% flagged for potential improvements.Leah Livingston, Director of Generative AI Evaluation for Health Markets at Elsevier, said: "These results reflect not just strong performance, but the real value of bringing clinicians into the evaluation process

PharmiWeb
May 7th, 2025
Elsevier Adds Half A Million Records From Clinicaltrials.Gov To Embase, Enabling A Seamless Search Experience In The World’S Most Comprehensive Biomedical Database

London, 6 May 2025 – Elsevier, a global leader in information and analytics, is announcing the addition of half a million records from ClinicalTrials.gov to its leading biomedical literature database, Embase. The integration will enable researchers to seamlessly view high-quality information on clinical research studies and their results alongside the peer-reviewed literature, in-press publications and conference abstracts already available in Embase. Researchers will be able to conduct more comprehensive evidence and literature searches while having the confidence they will never miss important updates relevant to their drug, therapy, or medical device. Clinical trials data is a vital component of biomedical literature search, helping pharmaceutical and medical device companies stay informed about the latest scientific advancements, regulatory requirements, and competitive insights to support evidence-based decision-making. However, gathering data from multiple sources is currently prone to errors and is time-consuming, such as the duplication of search results, which slows research and regulatory processes. As new therapies and devices are developed and RD organizations seek collaborative partners, they must undertake thorough searches and justifications of evidence