Engineering the
Silicon Synapse.

Ads ML Infrastructure

• Architected and pioneered the Budgeting Optimization System (BOS), leveraging Reinforcement Learning (RL) to dynamically allocate multi-billion dollar ad spends. Developed the reward functions and policy gradients that balanced advertiser ROI with platform liquidity in real-time auctions.
• Led the design of production-scale streaming pipelines for ad-click telemetry, enabling online learning and sub-minute model freshness. Integrated ScaNN-based retrieval and TFX orchestration to handle trillions of daily events with millisecond-level inference latency.
• Partnered deeply with Site Reliability Engineering (SRE) to achieve 99.999% availability for the core ads serving stack. Orchestrated global failover protocols and automated 'circuit-breaker' logic to protect revenue integrity during high-volatility market events.
• Set the multi-year technical roadmap for Ads Infrastructure, driving engineering standards for ML reproducibility and observability. Mentored senior engineers across the organization on the deployment of distributed RL agents in production.

Workspace (Chat & Drive)

• Reduced global deployment latency by 60% for Google Workspace by architecting a unified TFX-based CI/CD pipeline, enabling sub-day iteration cycles for production ML models.
• Scalability lead for Google Drive's collaboration engine; optimized p99 latency for real-time file state synchronization during a 300% surge in global traffic (2020–2021).
• Enhanced file discovery via semantic retrieval; integrated multi-signal ranking models into the Drive 'Priority' backend, significantly reducing 'time-to-file' for Enterprise users.