Summary
Prabhat Agarwal is a senior machine learning engineer with 11 years of experience building and scaling production ML systems that drive measurable business impact, including contributions that accounted for >15% of annual OKRs. He has led 0-to-1 initiatives in generative AI, recommender systems, and representation learning—architecting billion-parameter models and web-scale graphs (5B+ nodes, 60B+ edges) deployed across multiple product surfaces. A Stanford MSCS researcher with publications and invited talks, he bridges cutting-edge research and production via deep PyTorch and MLOps expertise, real-time serving, and large-scale data pipelines. At Pinterest he shipped unified slate-level generative recommenders and a universal GNN framework that materially improved engagement and ad efficiency; he now continues this trajectory as an OpenAI member of technical staff. Notably, his work often focuses on unifying siloed modeling approaches into single, deployable systems that both simplify infrastructure and amplify product impact.
11 years of coding experience
7 years of employment as a software developer
Bachelor of Technology (Hons.) Computer Science And Engineering, Bachelor of Technology (Hons.) Computer Science And Engineering at Indian Institute of Technology, Kharagpur
Master of Science (MS) with Distinction in Research Computer Science, Master of Science (MS) with Distinction in Research Computer Science at Stanford University
English, Hindi, Bengali, Spanish