Nathan Singiri is a software engineer with nine years of experience building large-scale systems, currently focused on GenAI infrastructure at Meta after recent roles in financial analytics and electronic trading at Bloomberg. He combines a quantitative background in mathematics and economics from UC San Diego with hands-on engineering across high-throughput, low-latency environments at Bloomberg and Goldman Sachs. Nathan has moved from core trading systems to architecting LLM infrastructure, demonstrating an ability to translate domain-specific performance requirements into robust production services. Based in Menlo Park, he brings practical experience shipping mission-critical systems in regulated financial settings and now applies that rigor to scaling generative AI platforms. Notably, his career path reflects a pattern of tackling complex, performance-sensitive problems and adapting those solutions to emerging AI infrastructure challenges.
10 years of coding experience
3 years of employment as a software developer
University of California, San Diego
High School Diploma, High School Diploma at Canyon High School
Contributions:10 commits, 10 pushes, 1 branch in 1 year 7 months
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