Built a Google wide adopted eval framework for analyzing LLM accuracy

Designed and implemented an automated chip allocation system, reducing capacity provisioning time from several days to under 5 minutes.

Spearheaded the GQM Debugging Agent, enhancing customer satisfaction through self-resolution of scheduling conflicts.

Implemented critical observability and failure detection for the Workload Management Service managing 1.5M resources and Quota Management Service (700k+ owned capacity units), ensuring system resilience for Google's largest AI workloads.

Supported the development of our service’s response to power capping events in datacenters

Instrumented 17 error logs with context to track Android Shorts watch failures

Triaged and fixed top-impacting issues, improving global Shorts Watch Time by X%

Identified logging inconsistencies across YouTube clients

Led standardization effort (adopted by 16 teams) for uniform metadata

Mapped errors to UX flows, improving triage speed

Reclassified error severities, reducing metric noise by 75%

Added real-time signals for YT Music & YTTV, detecting 5 Major to Huge outages over 4 months

Enabled pre-prod error detection to block regressions before launch

Built metric to track stuck unary/streaming RPCs

Created dashboards, alerting, and mitigation playbook for OnCall teams

Migrated observability from legacy internal tool to a new platform

Increased CPU limits on YT’s frontend load balancers, saving ~2 SWE/year

Added monitoring for optional dependencies returning degraded yet successful responses

Focused on revenue and UX-critical paths in YouTube’s frontend service

Mediated and provided conflict resolution in a professional and timely manner