Adam Grygielski
Senior Machine Learning Engineer at NVIDIA
Poland
Summary
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Rockstar🎓
Top SchoolAdam Grygielski is a Senior Machine Learning Engineer with eight years of experience applying deep learning to real-time graphics and computer vision, currently working on AI in Computer Graphics at NVIDIA. He previously served as a Principal Member of Technical Staff at AMD, where he led the Advanced Rendering Research group and drove algorithm design and model training for FidelityFX Super Resolution 4 (FSR4). Comfortable shifting between C++/graphics APIs and PyTorch with CUDA extensions, Adam focuses on enhancing rendering pipelines with neural solutions and is actively exploring neural representations like NeRFs and 3D Gaussians. His open-source contributions include integrating oneDNN acceleration into PaddlePaddle—optimizing activation functions and operator fusion for CPU performance—highlighting a strong background in low-level optimization. He brings hands-on leadership from industrial GPU teams and earlier roles at Intel and Luxoft, plus a practical engineering mindset honed by leading a student rocket electronics/software team. Based in Poland, Adam combines research-driven strategy with production-grade implementation across GPU, CPU, and ML stacks.
8 years of coding experience
8 years of employment as a software developer
Magister (Mgr), Big Data Analytics, Magister (Mgr), Big Data Analytics at Politechnika Wrocławska
English
Stackoverflow
11reputation
71reached
0answers
1question
Github Skills (9)
c-language10
cprogramming-language10
performance-optimization10
python10
neural-network9
machine-learning9
deep-learning9
distributed-training4
tensorflow4
Programming languages (4)
Github contributions (5)
PaddlePaddle/Paddle
Jul 2019 - Sep 2020
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:17 reviews, 54 commits, 69 PRs in 1 year 2 months
Contributions summary:Adam primarily contributed to the implementation of MKLDNN (oneDNN) support for the PaddlePaddle deep learning framework, with a focus on activation functions like LeakyRelu and Gelu. They integrated and optimized these activation functions using the oneDNN library, enabling improved performance through CPU-based acceleration. Their work included modifying operator implementations, and creating unit tests for verifying correct functionality. Furthermore, they expanded the fusion capabilities to combine convolutional layers with batch normalization and affine channel layers within the oneDNN framework.
pytorchpythonparalleldeep-learningpaddlepaddle
grygielski/incubator-mxnet
Oct 2020 - Jan 2021
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Contributions:7 pushes, 4 branches in 3 months
pythonschedulerdataflowmutationorchestration
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