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Clifton Collins

@cliftoncollins

Staff AI/ML Engineer at Meta who advanced Facebook and Instagram recommendations and cut p95 inference latency by 30%.

United States
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At Meta, I architected production AI/ML systems for Facebook and Instagram recommendation surfaces. I advanced feed and Reels recommendations with two-tower retrieval, learning-to-rank, and multimodal embeddings, while improving feature freshness latency by 60%.

I developed generative AI capabilities around Meta's Llama ecosystem, combining retrieval-augmented generation, vector search, and agentic orchestration. I also optimized production inference, lowering p95 latency by 30%, increasing serving throughput by 2.1x, and reducing compute cost per prediction by 20%.

At Realtor.com, I led personalization for home search and discovery, increasing search-result relevance by 10%. Earlier, I engineered banking data pipelines at JPMorganChase and developed property valuation and mortgage technology applications at CoreLogic.

Experience

Work history, roles, and key accomplishments

Meta logoME

Staff AI/ML Engineer & Data Scientist

Aug 2022 - Aug 2026 (4 years)

Architected large-scale production AI/ML systems for Facebook and Instagram recommendation surfaces, including feed and Reels platforms. Engineered streaming data pipelines and generative AI capabilities, optimizing inference and leading cross-functional technical delivery.

Realtor.com logoRE

Lead AI/ML Engineer

May 2019 - Jun 2022 (3 years 1 month)

Led personalization for home-search and discovery using learning-to-rank, collaborative filtering, and embedding models. Engineered scalable ML pipelines and operationalized high-traffic services with MLOps practices.

Education

Degrees, certifications, and relevant coursework

AU

Alcorn State University

Master of Science, Computer Science

2011 - 2015

Pursued a Master of Science in Computer Science, focusing on advanced computing topics.

AU

Alcorn State University

Bachelor of Science, Computer Science

2009 - 2011

Completed a Bachelor of Science in Computer Science, building a strong foundation in computing principles.

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