Adam Grabowski is an AI Frameworks Engineer at Intel with five years of experience optimizing deep learning runtimes and integrating high-performance primitives into production frameworks. Based in Gdynia, Poland, he has contributed to the widely used Apache MXNet project by adding oneDNN support for bfloat16 operators and scalar power, improving performance for reshape, concatenate and element-wise ops. He blends low-level systems thinking with practical backend engineering to make ML workloads faster and more portable across languages and platforms. Colleagues value his ability to navigate complex numerical kernels and deliver measurable speedups in real-world frameworks.
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
Back-end Developer
Contributions:69 reviews, 19 commits, 17 PRs in 1 year 4 months
Contributions summary:Adam primarily contributed to the optimization and extension of the MXNet framework, specifically focusing on integrating oneDNN support for various operators. Their work included adding support for reshape, concatenate, and element-wise binary operations (add, subtract, multiply, divide) with bfloat16 data types. The user also worked on integrating oneDNN support for scalar power operations. This indicates an effort to improve performance and expand the capabilities of the deep learning library.
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Contributions:103 pushes, 17 branches in 1 year 5 months
pythonschedulerdataflowmutationdata-science
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Adam Grabowski - AI Frameworks Engineer at Intel Corporation