This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
Satalia builds enterprise-grade AI systems for WPP and its FTSE 100 client base. Led by WPP Chief AI Officer Daniel Hulme, we run as a high-autonomy, decentralised organisation where engineers and scientists own their domains end to end. We are building AI systems that operate on terabyte-scale multimodal datasets to power the next generation of marketing intelligence. The Role: Our current work includes: Agentic pipelines — multi-step LLM systems with tool use, planning, and self-evaluation that automate complex marketing workflows end to end. Domain-adapted foundation models — fine-tuning open-weight LLMs (LoRA, RLHF, distillation) on proprietary WPP data for tasks like audience segmentation, creative scoring, and brand-safety classification. Retrieval-augmented generation — production RAG systems over large proprietary corpora (embedding models, vector indices, re-ranking) that serve real-time answers to client queries. Classical ML at scale — gradient-boosted models, causal inference pipelines, and recommendation engines that run alongside LLM components in hybrid architectures. This is a hands-on role where you will learn by working alongside experienced data scientists on real production systems that serve global clients. We invest heavily in developing our junior hires and will pair you with senior mentors who will help you grow into a strong, independent practitioner.
Job Responsibility:
Explore and prepare datasets — cleaning, feature engineering, and exploratory analysis across structured and unstructured data (text, image, tabular)
Train and evaluate ML models under the guidance of senior scientists, learning how to move from a working prototype to a production-ready system
Write and maintain Python code that runs in production — scripts, pipeline components, and data processing jobs — with support through code review
Help build and test components of LLM-powered systems: prompt templates, evaluation scripts, data loaders, and retrieval pipelines
Run experiments systematically: track hypotheses, log results, and communicate findings clearly to the team
Learn and adopt software engineering best practices — Git workflows, testing, documentation, and CI/CD — as part of your daily work
Requirements:
A degree in a quantitative field (computer science, mathematics, statistics, physics, engineering, or similar) or equivalent practical experience
Solid understanding of ML fundamentals: supervised vs. unsupervised learning, overfitting, evaluation metrics, and basic model selection
Working knowledge of Python — you can write functions, use libraries, debug errors, and read other people's code
Familiarity with core data science libraries (pandas, NumPy, scikit-learn)
Some project experience with ML — academic projects, personal projects, internships, or competition entries all count
Curiosity and initiative — you read papers, follow releases, tinker with new tools, and ask good questions
Clear communication — you can explain what you did, why, and what you learned from it
Nice to have:
Exposure to deep learning (NLP or computer vision) through coursework or personal projects
Familiarity with Git and command-line workflows
Experience with SQL or any data pipeline tooling
Interest in LLMs, prompt engineering, or generative AI — even if it's just personal experimentation
Contributions to open-source projects, Kaggle competitions, or a technical blog
What we offer:
Enhanced pension
Life assurance
Income protection
Private healthcare
Remote working
Truly flexible working hours
Generous Leave - 27 days holiday plus bank holidays and enhanced family leave
Annual bonus
Impactful projects - focus on bringing meaningful social and environmental change
People oriented culture
Transparent and open culture
Development - focus on bringing the best out of each other