Be the primary technical authority for our retrieval stack and move beyond incremental tuning to drive step-functions in ad relevance and quality. Lead a team of ML engineers to explore the frontiers of embedding-based retrieval, deep learning architectures, and multi-objective optimization (user value vs. advertiser success).

Define the roadmap for retrieval quality. This includes moving from legacy retrieval methods (e.g., two-tower models, transformer-based embeddings, and generative retrieval).

Innovate on how we measure and improve relevance. Lead efforts to align retrieval outputs with long-term user satisfaction and advertiser Return on Investment (ROI) ensuring we aren't just retrieving clicks, but value.

Design next-generation applicant generation strategies. Oversee the integration of fresh signals (user intent, content semantics, and social trends) into our retrieval models to capture the dynamic nature of the discover feed.

Bachelor’s degree or equivalent practical experience.

8 years of experience with software development in one or more programming languages.

7 years of experience testing, maintaining or launching software products, and 1 year of experience with software design and architecture.

7 years of experience with AI/ML algorithms and tools, deep learning and AI/ML modeling.

Experience building and deploying recommendation systems models (e.g., retrieval, prediction, ranking, personalization, search quality, embedding) in production.