Rahul Tuli
Software Engineer (ML) at Red Hat
Somerville, Massachusetts, United States
Summary
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Rockstar🎓
Top SchoolRahul Tuli is a software engineer specializing in machine learning with 9+ years of core CS experience and 3+ years focused on ML, currently working at Red Hat after Neural Magic’s acquisition. He has deep practical experience in efficient ML inference on CPUs—contributing benchmarking, deployment, and client-server tooling to the well-known Neural Magic Deepsparse project for transformer models. Rahul combines strong engineering discipline with applied research from roles building ASR models and production-ready ML pipelines, and he’s particularly interested in using AI to improve accessibility. Known for turning complex ideas into practical benchmarks and examples, he also brings a curiosity for efficient computing and a knack for clear problem framing (and an appreciation for warm blueberry muffins).
9 years of coding experience
6 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at Rochester Institute of TechnologyBachelor of Technology - BTech Information Technology, Bachelor of Technology - BTech Information Technology at Manipal Academy of Higher Education
Stackoverflow
1reputation
0reached
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Github Skills (12)
transformers10
machine-learning10
benchmark10
benchmarking10
huggingface10
inference10
onnx10
python10
nlp9
pruning9
computer-vision8
quantization8
Programming languages (5)
Github contributions (5)
neuralmagic/deepsparse
Jul 2021 - Jan 2023
Role in this project:
ML Engineer
ML EngineerContributions:735 reviews, 112 commits, 247 PRs in 1 year 6 months
Contributions summary:Rahul primarily focused on enhancing the benchmarking and deployment aspects of transformer models within the `deepsparse` framework. Their contributions include creating example scripts for benchmarking Hugging Face BERT models, integrating with the new DeepSparse pipeline API, and developing client-server interactions. Furthermore, the user was involved in refactoring and optimizing the benchmark scripts to utilize real data or data samples from sparsezoo, alongside updating benchmark arguments and improving the overall structure of the examples.
llm-inferenceruntimetensorflowsparsificationmachinelearning
rahul-tuli/Searches
Oct 2019 - Jan 2021
A repo containing python code for Informed and Uninformed searches
Contributions:21 commits, 4 PRs, 17 pushes in 1 year 3 months
informedpython-codesearchespython
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