Bowen Hong
@bowenhong
Staff engineer transforming LLM and retrieval systems into reliable, large-scale products.
United StatesWhat I'm looking for
I’m a Staff Software Engineer with 10+ years designing and operating large-scale distributed systems, AI/ML platforms, and retrieval systems at Meta, Google, and Amazon. I focus on taking ambiguous user needs and turning them into production-ready AI systems—especially LLM/RAG architectures, evaluation pipelines, and real-time processing that improve answer quality and reliability.
At Meta, I led end-to-end AI-powered assistant and retrieval system work at massive scale (100M–3B users), including low-latency indexing/retrieval (8M QPS), real-time streaming migrations (Spark → Flink), and an experimentation platform enabling 2,000+ concurrent ML/LLM experiments daily. I also drive technical reliability (99.97% SLA), cross-team architecture decisions, and engineering excellence through mentoring and rigorous design/code reviews.
Experience
Work history, roles, and key accomplishments
Led design and deployment of an AI-powered assistant and an ML Ads Ranking platform, improving answer quality and reducing defect escape rate by 31% while accelerating model iteration by 43%. Built and operated a low-latency retrieval system at 8M QPS, delivered an evaluation/experimentation platform (2,000+ concurrent experiments daily), and migrated batch to real-time streaming (Spark to Flink),
Built and scaled distributed indexing and search pipelines ingesting 2B+ items daily with sub-minute freshness, improving retrieval accuracy and latency. Developed real-time data pipelines and an ML-based detection system that blocked 94M fraudulent listings per month, and optimized distributed query systems to reduce p99 latency by 38%.
Developed an anomaly detection system (Java, Lambda, DynamoDB) for customer usage insights, reducing support escalations by 19%. Automated distributed infrastructure provisioning and contributed to distributed database testing (Aurora) to improve robustness before launch.
Built an A/B experimentation framework for recommendation systems, improving experiment velocity and driving conversion-rate gains. Prototyped a real-time analytics system for user behavior insights to inform product and ML decisions.
Education
Degrees, certifications, and relevant coursework
University of Michigan
Master of Science in Engineering, Computer Science
2014 - 2015
Completed a Master of Science in Engineering focused on Computer Science at the University of Michigan (2014–2015).
Shanghai Jiao Tong University
Bachelor of Science in Engineering, Electrical and Computer Engineering
2010 - 2014
Earned a Bachelor of Science in Engineering in Electrical and Computer Engineering at Shanghai Jiao Tong University (2010–2014).
University of Michigan
Bachelor of Science in Engineering, Computer Science
2012 - 2013
Studied Computer Science as part of a Bachelor of Science in Engineering program at the University of Michigan (2012–2013).
Tech stack
Software and tools used professionally
Availability
Location
United StatesAuthorized to work in
Job categories
Skills
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