Amardeep Kumar
I love maths, coding, and Machine Learning. I work at the intersection of AI research and engineering — tackling reasoning, alignment, and hallucination in LLMs across visual question-answering, spatio-temporal reasoning, code-switching, and multi-modality.
Currently a Software Engineer at DoorDash. Recently graduated from NYU Courant (MS CS, AI specialization). Previously built ML infrastructure at Instabase and Walmart, interned at PineGap.ai, and participated in Google Summer of Code. Published at EMNLP, ACL, and CIKM.
One day I want to start a profitable and calm business solving niche AI research problems.
Featured
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GenZ to AI Enz: A Roadmap for CS Grads Breaking into AI
A complete series taking CS students and early-career engineers from zero ML knowledge to building real AI systems with LLMs and agents.
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How We Cut ML Inference Latency by 40% on Kubernetes
The architecture behind our async model serving platform at Instabase — async workers, RabbitMQ, multi-level caching, and sticky routing to cut inference time by 40%.
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GupShup: Summarizing Code-Switched Conversations
Our EMNLP 2021 paper on abstractive summarization of Hindi-English code-switched conversations — introducing the GupShup dataset.
Recent Posts
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Concurrency, Demystified: What Every Engineer Should Actually Know
A from-fundamentals tour of concurrency in Python, told as one connected story instead of a glossary of terms — threads vs. processes, the GIL, locks, condition variables, asyncio, distributed rate limiting, and a staged concurrent web crawler exercise.
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Distributed Training at Scale: Parallelism, Memory, and the Optimizer Stack
How DDP, tensor/pipeline/sequence parallelism, ZeRO/FSDP, activation recomputation, mixed precision, and the optimizer stack combine to train models no single GPU could hold.
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Seq2Seq: Teaching Machines to Translate
The encoder-decoder architecture that powered translation before Transformers, the fixed-size bottleneck it created, and the idea that finally fixed it.
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CNN, RNN, LSTM: The Road to Transformers
The architectures that came before Transformers, the limits they hit, and why each new design was needed to fix the previous one.