Min Liu

Senior Software Engineer, Meta Platforms

Email: minliu@meta.com
Location: United States
LinkedIn

About

I am a Senior Software Engineer at Meta, where I work on Instagram's advertising platform. My work focuses on building AI-driven systems that improve advertising effectiveness for millions of businesses while advancing privacy-preserving measurement, algorithmic fairness, and infrastructure reliability at scale.

I am broadly interested in the intersection of machine learning, privacy-enhancing technologies, and large-scale distributed systems. My goal is to advance digital advertising into a privacy-first, AI-driven ecosystem that supports inclusive economic growth, protects user trust, and strengthens U.S. technological leadership.

Focus Areas

AI-Driven Advertising Optimization — Deep-learning conversion models and bidding systems that improve advertiser return on ad spend, with particular impact on small and medium-sized businesses
Privacy-Enhancing Technologies — Federated analytics and secure aggregation architectures that enable effective ad measurement without exposing individual user data, addressing post-ATT signal challenges
Algorithmic Fairness & Transparency — Real-time bias detection and explainability systems for advertising delivery, aligned with evolving regulatory and civil-rights standards
Large-Scale Distributed Systems — Observability, anomaly detection, and reliability engineering for high-throughput advertising infrastructure
Generative AI for Commerce — AI-powered conversational experiences that connect consumers with products through natural-language interaction

Experience

Senior Software Engineer

Meta Platforms — Instagram Ads

Present

Building AI-driven advertising systems at scale. Leading work on conversion optimization, privacy-preserving measurement, fairness monitoring, and platform reliability for Instagram's advertising infrastructure. Contributing to open-source ML frameworks and privacy-enhancing technology initiatives.

Selected Work

Privacy-Preserving Ad Measurement

Designed and deployed a federated analytics pipeline that measures advertising effectiveness using on-device computation and secure aggregation, enabling accurate measurement without centralizing individual user data. This architecture addresses the industry-wide challenge created by mobile platform privacy changes and serves as a reference design for privacy-compatible advertising analytics.

Novel Audience-Matching Methods

Developed audience-matching approaches that move beyond conventional demographic and behavioral targeting paradigms. These methods capture dimensions of consumer intent that traditional models are structurally unable to access, delivering improved conversion rates and lower acquisition costs for advertisers, including millions of U.S. small businesses.

Real-Time Fairness Monitoring

Built automated fairness-monitoring systems that continuously audit advertising delivery decisions for discriminatory patterns in real time. The system flags potential bias in housing, employment, and financial advertising, supporting compliance with federal civil-rights standards and FTC guidance on algorithmic accountability.

Advertising Infrastructure Reliability

Architected distributed tracing and anomaly-detection systems that maintain high availability for advertising delivery at scale. The infrastructure supports mission-critical use cases including public health campaigns, emergency communications, and small-business advertising.

Short-Form Video Monetization

Contributed to ranking and pacing algorithms for short-form video advertising, improving monetization efficiency and helping transform an engagement-focused format into a sustainable revenue contributor.

Generative AI for Product Discovery

Developing AI agent prototypes for conversational commerce experiences that combine large-language-model retrieval with product-graph embeddings, enabling natural-language product discovery and recommendation.

Education

M.S. Advanced Architectural Design

Carnegie Mellon University

2020

Broader Impact

My work contributes to several areas of national importance. The privacy-preserving measurement systems I build help establish industry standards for privacy-compatible advertising analytics, addressing concerns raised in bipartisan Congressional hearings and aligning with emerging U.S. data-protection legislation. My fairness monitoring tools support equitable ad delivery and civil-rights protections in digital commerce. The advertising optimization techniques I develop lower customer-acquisition costs for millions of American small businesses, supporting local entrepreneurship and economic growth.

I contribute to open-source machine learning frameworks and privacy-enhancing technology libraries, accelerating the diffusion of these innovations across the broader U.S. technology ecosystem — from university research laboratories to startups and government applications.