Summary
Tejas Srivatsav is a Machine Learning Engineer with 8 years of experience building and shipping 20+ production-grade ML systems across infrastructure, applied ML, and analytics, currently working on large-scale ML infrastructure at Meta. He excels at turning ambiguous product goals into measurable outcomes, designing low-latency, petabyte-scale data pipelines and deploying agentic AI for rapid root-cause analysis that materially sped incident resolution. His background spans end-to-end ML lifecycle work—from data pipelines and model training to evaluation, robustness, and deployment—backed by an MS from Carnegie Mellon and a BS from UC Berkeley. Tejas has a track record of practical innovation, including hybrid search ranking to boost recommender recall, event-driven triggers to improve data freshness, and fine-tuning LLMs for domain-specific code translation. Comfortable across Python, C++, Spark, CUDA and cloud platforms, he bridges theory and systems engineering to deliver scalable, data-driven products. Based in Menlo Park, he’s particularly focused on data quality, evaluation, and making large-scale ML systems reliable and actionable.
8 years of coding experience
3 years of employment as a software developer
Bachelor of Science - BS Electrical Engineering and Computer Science, Bachelor of Science - BS Electrical Engineering and Computer Science at University of California, Berkeley
High School Diploma, High School Diploma at Gulliver Preparatory School
Master of Science - MS Electrical and Computer Engineering - Concentration in Machine Learning, Master of Science - MS Electrical and Computer Engineering - Concentration in Machine Learning at Carnegie Mellon University
Mathematics, Mathematics at University of Miami
English, Tamil