AI/ML Engineer
Posted 10 Feb 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 5–7 yrs
- Management level
- Individual contributor
- Employment
- Hybrid
No salary listed for this role.
Field of studyArtificial Intelligence, Computer Science, Data Science (or related)
Study levelBachelor's
Who We Are
VML is a global creative company that combines brand experience, customer experience, and commerce to create connected brands that drive growth. Guided by a Human First philosophy, VML is known for its innovative work for brands and consistently ranks among the world’s leading creative companies, including the WARC Creative 100. VML’s global network spans 55+ markets with principal offices in Buenos Aires, Kansas City, London, New York, São Paulo, Shanghai and Singapore.
VML is part of WPP, the trusted growth partner for the world's leading brands. Powered by exceptional talent and our agentic marketing platform WPP Open, WPP unites cutting-edge media intelligence and data solutions, creativity, production, enterprise solutions and expert strategic counsel.
For more information, please visit www.vml.com and follow along on LinkedIn #WeAreVML.
Who we’re looking for: We are seeking an experienced AI/ML Engineer with a minimum of 5 years of expertise to join our innovative technology team. In this pivotal role, you will be responsible for designing, training, and optimizing AI models, with a strong focus on Natural Language Processing (NLP) for real-time content summarization, recommendation, and conversational UIs. You will implement cutting-edge techniques like Retrieval-Augmented Generation (RAG) and fine-tune transformer-based models, collaborating closely with backend and data engineering teams to ensure seamless deployment and maintenance of brand tone and content accuracy in all AI outputs.
What you’ll be doing:
- Design, train, and optimize AI models specifically for real-time content summarization and recommendation.
- Implement advanced Natural Language Processing (NLP) techniques to enhance conversational UI and predictive interactions.
- Apply Retrieval-Augmented Generation (RAG) for intelligent response generation from dynamic content sources.
- Fine-tune transformer-based models (e.g., BERT, T5) using domain-specific datasets to achieve optimal performance.
- Collaborate closely with backend and data engineering teams to streamline model deployment within a production-grade environment.