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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. 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)
Exposure to PyTorch or TensorFlow is a plus
Some project experience with ML — academic projects, personal projects, internships, or competition entries all count. Show us something you've built
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:
Remote working - café, bedroom, beach - wherever works
Benefits healthcare
Truly flexible working hours - school pick up, volunteering, gym
Generous Leave – holidays in line with Greek Law, plus bank holidays and enhanced family leave
Impactful projects - focus on bringing meaningful social and environmental change
People oriented culture - wellbeing is a priority, as is being a nice person
Transparent and open culture - you will be heard
Development - focus on bringing the best out of each other