Harsh K.

About Harsh K.

Seasoned Technical Program Manager with 15+ years leading high performance large-scale distributed systems infra, data platforms, cloud infrastructure, AI/ML, stream processing and database initiatives at Meta, LinkedIn, Apple, and Yahoo. Proven track record of delivering 0→1 platforms (TiDB, Pro-ML, Quasar), cross-org migrations, and company-wide compute efficiency programs saving over $1.2B in CapEx and OpEx costs.Career Highlights:* Core Infra Capabilities: Spearheaded and delivered TiDB as LinkedIn’s next-gen 0→1 strongly consistent distributed database with ACID guarantees as a core infra offering; curated a pipeline of customers; onboarded key internal product Euler, a high leverage developer productivity platform, enabling product engineers to spin up new entities and queries on LinkedIn in hours instead of weeks, reducing engineering toil and accelerating product velocity. Demoed features and presented progress bi-weekly to the CTO and his staff.* Cross-Organizational Leadership: Initiated and led a program to migrate Samza jobs to Flink on Kubernetes, enabling ML engineers to unify batch and streaming pipelines, deliver real-time features and accelerate model experimentation velocity, directly improving relevance in feed, search, ads and recommendations.* AI/ML High-Performance Systems: Initiated and drove the development and adoption of high-performance ML scoring and ranking system (Quasar) across 40+ verticals at LinkedIn, improving leverage for relevance across search, feed, recommendation systems, and ads verticals.* AI/ML Programs – Ran advanced AI/ML infrastructure programs, including Pro-ML, at LinkedIn, enhancing system capabilities and leverage.

Experience

1. Technical Program Manager

   Meta

   Mar 2025 — Present · Menlo Park, CA, US

   Driving Meta’s company-wide Capacity & Efficiency program. Enabling engineering teams to deliver measurable reductions in MW consumption through targeted technical optimizations across the full stack to meet Meta’s capacity requirements for AI.* Building and deploying automated tooling to identify efficiency wins opportunities and detect regressions, integrating insights into engineering workflows for proactive mitigation.* Launching Efficiency Champions, a company-wide program with platform and product group leaders, to create a framework for efficiency and regression prevention and fostering a culture of compute efficiency to help Meta archive product goals amid datacenter capacity constraints.

Education

• Cornell University

  Doctor of Philosophy - PhD - On Leave, Computer Science

• Cornell University

  Bachelor of Science - BS, Computer Science