Mukul Kumar is a Staff Machine Learning Engineer with nine years of experience building production-grade ML and backend systems across Meta, Amazon, Microsoft, and Salesforce. He has repeatedly shipped transformer-based NLP solutions—spanning search query classification, multilingual models, and compressed/distilled encoders—for large-scale product search and voice applications. At Amazon he led cross-team efforts to create buyer/seller representations and launch query rewrite and spell-correction models, and he holds multiple patents and conference publications in applied NLP. More recently he led agentic and generative-AI work for developer tooling and ranked highly on SWEBench, demonstrating a knack for both research and rigorous engineering. Mukul combines deep research instincts with pragmatic system design—optimizing latency, compression, and deployability—to make ML models useful in production. Based in the United States, he prefers roles that bridge applied research and end-to-end engineering to deliver measurable business impact.
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
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