I am a Senior Software Engineer at Meta working on infrastructure for Facebook Feed Ranking. With over 8 years of experience building and scaling distributed systems at major tech companies, my expertise spans backend services, data infrastructure, cloud computing, and system optimization. I have a proven track record of delivering high-impact solutions that process billions of transactions and manage thousands of servers.
My path into infrastructure engineering wasn’t a straight line. I started with network security at Trend Micro in Taipei, moved to the US for grad school, and quickly realized that the problems I loved most were about scale. How do you make systems work when “a few users” becomes “a few billion”?
At eBay, a data science internship showed me the power of building systems that catch what humans can’t. In that case, fraud detection models processing millions of transactions. That’s when I understood that the real leverage in engineering isn’t writing clever code; it’s building systems that compound.
AWS was where I grew up as an engineer. Five years on the Billing team taught me what it means to build something that absolutely cannot be wrong. When your system handles the financial records for every AWS customer, “good enough” doesn’t exist. I designed the unbilled usage auditor that caught $125,000 in discrepancies and reduced them to $432, a 300x improvement. That project taught me to think in terms of correctness guarantees, not just functionality.
Twitter was a different kind of challenge: operating at scale under pressure. Managing kernel updates across 5,000+ production servers means you can’t afford to break things, but you also can’t afford to fall behind. I built the automation and validation tooling that made this possible, and resolved 140+ support tickets in a single on-call week. That taught me as much about prioritization as it did about systems.
At Walmart, I shifted to data infrastructure, leading the Data Lake integration for recruiting systems. That meant connecting 10+ data sources through Spark, Kafka, and Hudi pipelines. It was my first time leading an initiative at enterprise scale from design through delivery.
Now at Meta, I’m building the infrastructure that powers Facebook Feed Ranking, the system that decides what 2 billion people see when they open Facebook. It’s the intersection of everything I’ve worked on: high-performance C++ services, ML infrastructure, and systems that have to work at a scale most engineers never encounter.
I’ve worked at enough companies to know what I care about:
The invisible layer. I’m drawn to infrastructure, the systems nobody sees until they break. There’s something satisfying about building something that billions of people rely on without ever knowing your name.
Correctness over cleverness. The AWS billing experience permanently changed how I think. I’d rather have a boring system that’s reliably correct than an elegant one that’s probably correct.
Making teams faster. The best infrastructure work isn’t about building systems. It’s about removing friction for the engineers who depend on your systems. Every automation I build, every tool I create, is measured by how much it unblocks others.
Designed and built a distributed system that detects unbilled usage across all AWS services — reducing charge discrepancies by 300x and eliminating 230 million monthly false positives. Key Metrics # 300x Reduction in Discrepancies $125,000 → $432 230M False Positives Eliminated ~95% Alert Actionability Architecture # flowchart LR A["Usage Records\n(Billions/day)"] --> B["Smart Sampling\n& Aggregation"] B --> C["Multi-Signal\nValidation"] C --> D["Automated\nResolution"] D --> E{Real issue?} E -- Yes --> F["Alert with\nDiagnosis"] E -- No --> G["Auto-resolve\n& Log"] style B fill:#6366f1,color:#fff style C fill:#6366f1,color:#fff style D fill:#6366f1,color:#fff Technical Deep Dive # Aggregation over Brute-Force # Instead of checking every individual usage record (which generated 230M false positives), the system aggregates at the service-account-period level.
Built the automation and validation tooling to manage kernel updates across 5,000+ production servers at Twitter — with zero-downtime progressive rollouts and automated canary validation. Key Metrics # 5,000+ Production Hosts Weeks → Days Rollout Time 140+ Tickets in One On-Call Week Zero-Downtime Updates Architecture # flowchart LR A["New Kernel\nVersion"] --> B["Canary\nValidation"] B --> C["Wave 1\n1% Fleet"] C --> D["Wave 2\n5% Fleet"] D --> E["Wave 3\n25% Fleet"] E --> F["Full Fleet\nRollout"] C -- anomaly --> G["Pause &\nAuto-Alert"] D -- anomaly --> G E -- anomaly --> G style B fill:#6366f1,color:#fff style C fill:#6366f1,color:#fff style D fill:#6366f1,color:#fff style E fill:#6366f1,color:#fff style F fill:#6366f1,color:#fff Technical Deep Dive # Validate Before You Roll # A Python library that validates kernel safety before fleet-wide rollout:
Obsidian + Claude Code is everywhere right now. But pointing an AI at a folder of markdown files and hoping for the best doesn’t work. What matters is how you structure the knowledge base. Get that right, and Claude becomes genuinely useful. Get it wrong, and you get confident garbage. There’s been a wave of posts about this combo lately: James Bedford’s full walkthrough, Greg Isenberg’s “personal OS” approach, kepano (Obsidian’s CEO) sharing Claude Skills. They’re all worth reading.