Jambay Kinley
Research Intern at Harvard NLP
Cambridge, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolJambay Kinley is a research-focused software engineer with four years of experience blending machine learning engineering and academic research, currently a Research Intern at Harvard NLP in Cambridge. He has hands-on contributions to high-impact open-source ML infrastructure, notably enhancing Microsoft’s ONNX Runtime with advanced quantization (FP4, NF4), bfloat16 support, and improved symbolic shape inference—work that improves inference performance and model compatibility. His background includes teaching fellowships in machine learning and theory at Harvard, reflecting a strong ability to explain complex concepts and mentor others. A former national top-ranked student, he brings both rigorous theoretical training and practical systems-level improvements to ML tooling and model deployment.
4 years of coding experience
1 year of employment as a software developer
Junior High School, Junior High School at Dagapela MSSBachelor of Arts - BA, Computer Science, Bachelor of Arts - BA, Computer Science at Harvard UniversityHigh School Diploma, Science Stream, National Topper 12th Grade Board Examinations, High School Diploma, Science Stream, National Topper 12th Grade Board Examinations at Ugyen Academy HSS
Stackoverflow
316reputation
63kreached
9answers
0questions
Github Skills (19)
python10
machine-learning10
onnx10
deep-learning10
neural-network10
cuda10
quantization10
c-language9
hardware-acceleration9
cprogramming-language9
tensorflow8
pytorch8
eclipse6
configurations6
thread-dump6
Programming languages (4)
Github contributions (5)
microsoft/onnxruntime
Mar 2022 - Mar 2025
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Role in this project:
ML Engineer
ML EngineerContributions:16 reviews, 23 PRs, 23 pushes in 3 years 1 month
Contributions summary:Jambay made significant contributions to the ONNX Runtime's machine learning inference capabilities. Their work included adding support for new quantization techniques like FP4 and NF4 within the MatMulBnb4 operator, as well as integrating bfloat16 support to enhance performance. They also addressed shape inference issues in the symbolic shape inference for ops like SkipGroupNorm and ReduceMean, ensuring compatibility with a broader range of models and improving the quantization tool. Furthermore, the user fixed an issue in the quantization tool's argparser and addressed issues with the calibration process.
runtimetrainingtensorflowai-frameworkaccelerator
microsoft/Olive
Mar 2023 - Apr 2025
Olive: Simplify ML Model Finetuning, Conversion, Quantization, and Optimization for CPUs, GPUs and NPUs.
Contributions:2 releases, 2276 reviews, 889 PRs in 2 years
pythonautomatesruntimecorrectnessmodel-conversion
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