User Researcher jobs in United Kingdom

Ads is the largest revenue generator at Meta and Ads Quality represents around 20% of total revenues which are used to generate long term ads and organic engagement. Core Ads Quality is a unique team jointly optimizing for both quality and revenue, aiming at making this investment more revenue / quality trade‑off efficient and generate long term revenue growth through user learning. Among others, Core Ads Quality focuses on: - Finding the right trade‑off between short and long term revenues - Standardising and optimise quality treatment of ads across surfaces and page types - Understanding user behaviour with respect to ads quality - Building a solid infrastructure around signals, labels and quality metrics We work at the intersection of Ads, Machine Learning and User Behaviour understanding. The nature of our work is very analytical, with a solid collaboration with our Data Scientist and a heavy focus on not only understand "what" but also "why". Despite having been created a couple of years ago, the Ads Quality space at Meta is still nascent and full of unexploited opportunities. The organization is further structured into the following teams/sub‑pillars: - Integrity & Efficiency: Proactively cover long‑term revenue risks from advertiser friction while supporting XI with delivery expertise. - Ads Conversion Familiarity: Accelerate Non‑Purchaser (NP) → Purchaser (P) transition by increasing familiarity of ads for users who don't interact with ads frequently. - Post‑Click Quality: Stop Purchaser (P) → Non‑purchaser (NP) user conversions from bad purchase experiences. - Modelling: Enhance quality and drive long‑term revenue growth through modelling. - Quality Science: Build the foundational end to end understanding for Funnel quality signals to ensure its the efficiency, health and coverage. The team has consistently hit their goals and delivered XXXM$ in incremental long‑term revenue for Meta while ensuring high ads quality.

• 1. Apply relevant AI and machine learning techniques to build Ads auction/user behaviour treatments
• 2. Develop novel, accurate AI algorithms and advanced systems for large‑scale applications
• 3. Work towards long‑term ambitious production goals, while identifying intermediate milestones
• 4. Directly contribute to experiments, including designing experimental details, developing reusable code, running evaluations, and organizing results
• 5. Work with large data, and contribute to development of large scale foundation models
• 6. Design methods, tools, and infrastructure to push forward the state of the art in AI

• 7. Currently has or is in the process of obtaining a PhD in Artificial Intelligence (AI), Computer Science, or a related technical field
• 8. Experience developing machine learning algorithms or machine learning infrastructure in Python, PyTorch, and/or C/C++
• 9. Experience in training, fine‑tuning, and/or experimenting with foundation models beyond black‑box use

• 10. Experience in Reinforcement Learning, GenAI, Large Language Models, etc
• 11. Experience in Ads, especially in auction theory and implementation (bidding, budgeting, targeting)
• 12. Experience in User Behaviour modelling, Long‑term Value optimization or Causal Learning

Industry: Internet