Meisam Hejazinia
Principal Applied Scientist @Amazon
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WORK HISTORY
Principal Applied Scientist @Amazon
San Diego, CA, US
Driving innovation in supply chain optimization technology (SCOT) through advanced agentic LLM, machine learning, and reinforcement learning to enhance coding, reasoning, math, and writing use cases, for audit at scale, and root cause analysis.
EDUCATION
Amirkabir University of Technology - Tehran Polytechnic
Master's degree, Distributed Data Engineering
Naveen Jindal School of Management, UT Dallas
Doctor of Philosophy (PhD), Quantitative Marketing and Management Science
Amirkabir University of Technology - Tehran Polytechnic
Bachelor of Science (B.Sc.), Computer Software Engineering
Amirkabir University of Technology - Tehran Polytechnic
Bachelor of Science (B.Sc.), Management Information Systems, General
Naveen Jindal School of Management, UT Dallas
Master of Science (M.Sc.), Marketing Analytics
Sharif University of Technology
Master of Business Administration (M.B.A.), Management and Economics
ABOUT MEISAM HEJAZINIA
I build agentic LLM systems and the evaluation/governance that makes them trustworthy and cost-efficient. At Amazon I ship production agents (self-RAG, micro-SOPs, LLM-as-judge, RL-style optimization) for analytics and root-cause use cases. Earlier at Google and Meta I delivered ranking, creative/eval, and privacy-preserving ML (FL, DP, MPC) for large-scale products.I’m a builder-operator: rapid problem discovery → pragmatic architecture → measurable wins (quality/latency/cost). I’ve also taken products from 0→1 as a founder (ProVendee) and enjoy ops-heavy domains where ML directly moves business KPIs.Interests: agentic LLM platforms, eval/observability, retrieval & knowledge systems, RL/RLHF/RLVR, privacy-preserving ML, and AI for real-world ops.Background: PhD (Quantitative Management Science). Publications in ICML/TPDP/Recsys/POMS; SSRN top-10% author (2019).
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