Machine Learning Engineering Manager – LLM Serving, Infrastructure

• Lead a high-performing engineering team to develop, build, and deploy a high-scale, low-latency LLM Serving Infrastructure. • Drive the implementation of a unified serving layer to support multiple LLM models and inference types (batch, offline eval flows and real-time/streaming). • Lead all aspects of the development of the Model Registry for deploying, versioning, and running LLMs across production environments. • Ensure successful integration with the core Personalization and Recommendation systems to deliver LLM-powered features. • Define and champion standardized technical interfaces and protocols for efficient model deployment and scaling. • Establish and monitor the serving infrastructure's performance, cost, and reliability, including load balancing, autoscaling, and failure recovery. • Collaborate closely with data science, machine learning research, and feature teams (Autoplay, Home, Search, etc.) to drive the active adoption of the serving infrastructure. • Scale up the serving architecture to handle hundreds of millions of users and high-volume inference requests for internal domain-specific LLMs. • Drive Latency and Cost Optimization: partner with SRE and ML teams to implement techniques like quantization, pruning, and efficient batching to minimize serving latency and cloud compute costs. • Develop Observability and Monitoring: build dashboards and alerting for service health, tracing, A/B test traffic, and latency trends to ensure consistency to defined SLAs. • Contribute to Core LPM Serving: focus on the technical strategy for deploying and maintaining the core Large Personalization Model (LPM). Apply tot his job