Senior Software Specialist - AI / ML - Ads

Core Delivery Products is the Ads pillar that builds the products that power the core capabilities of our ads system: from targeting, to bidding, optimization, and user quality. The team has a track record of making critical contributions to company top line goals with most of Meta revenue flowing through these core systems, and our year on year improvements continuing to drive significant top-line impact. In EMEA, we have 3 complete sub-pillars operating with high autonomy: Targeting, Core Ads Quality, & Direct Response Optimization. All the sub-pillars are responsible for growing revenue, machine learning product solutions end to end, alongside the reliability and stability of these core products. All have their cross functional teams co-located with them.

7. Experience developing machine learning algorithms or machine learning infrastructure in Python, PyTorch, and/or C/C++
8. Bachelor's degree in Artificial Intelligence (AI), computer science, related technical fields, or equivalent practical experience
9. Experience in bringing research results into production
10. Experience in training, fine-tuning, and/or experimenting with foundation models beyond black-box use
11. Track record delivering successful products with large scale impact, 12. Experience in Ads, especially in auction theory and implementation (bidding, budgeting, targeting)
13. Experience in User Behavior modeling, Long-term Value optimization, or Causal Learning
14. PhD in Artificial Intelligence (AI), computer science, related technical fields, or equivalent practical experience
15. Experience in Reinforcement Learning, GenAI, Large Language Models, etc

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 and build methods, tools, and infrastructure to advance AI capabilities