San Francisco | California | United States
This team operates over a very large scale of hundreds of millions of requests and billions of documents. Our next steps are reducing E2E content distribution delay, applying cutting-edge technologies to support various types of content retrieving and dramatically improving our results by customizing them to a particular user. We will build E2E Realtime/Near Realtime content ingestion and serving infrastructure, need to support embedding based retrieving and build full stack capable of combining traditional information retrieval technologies with efficient user personalization models. Besides, we need to keep improving performance, stability and cost efficiency of the core system.
What you'll do:
What we’re looking for: