Derek Murray
Principal Software Engineer at Google DeepMind
Redwood City, California, United States
Summary
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Rockstar🎓
Top SchoolDerek Murray is a Principal Software Engineer with 19 years of experience designing and optimizing large-scale ML and systems software, currently based in Redwood City and working at Google DeepMind. He helped create TensorFlow and later drove performance and compatibility improvements across high-profile open-source projects such as tensorflow/tensorflow and google-research, with deep expertise in tensor operations, data pipelines, and TPU tooling. Derek combines research rigor (PhD from Cambridge) with production-grade engineering, having shipped core optimizations for sparse tensor ops and execution engines that materially reduce allocations and lock contention. His career spans senior technical leadership roles at Google and Microsoft and hands-on contributions to high-performance recommendation and TPU codebases, showing a rare blend of systems-level tuning and ML engineering. Notably, his open-source commits reflect a focus on modernizing TensorFlow data pipelines and replacing deprecated APIs to keep research codebases compatible and efficient. He is adept at turning academic systems research into practical, high-performance infrastructure that scales in cloud environments.
19 years of coding experience
15 years of employment as a software developer
MSc High Performance Computing, MSc High Performance Computing at The University of EdinburghPhD Computing Science, PhD Computing Science at University of CambridgeBSc (Hons) Computing Science, BSc (Hons) Computing Science at University of Glasgow
Stackoverflow
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Github Skills (20)
c-language10
data-pipelines10
python10
machine-learning10
tpu10
data-preprocessing10
deeplearning-ai10
deep-learning10
tensorflow10
performance-optimization10
scalability10
cprogramming-language10
data-pipeline10
optimization10
distributed-training9
Programming languages (9)
Github contributions (5)
tensorflow/tensorflow
Dec 2015 - May 2020
An Open Source Machine Learning Framework for Everyone
Role in this project:
Back-end Developer
Back-end DeveloperContributions:4 reviews, 1170 commits, 189 PRs in 4 years 5 months
Contributions summary:Derek focused on optimizing the performance of the `ConvertToCooTensorOp` operation, addressing computational and allocation issues. They implemented several improvements, including conditional gain-rescaling avoidance and efficient handling of scalar weights. Furthermore, the user made improvements in the code for extracting row IDs from SparseTensor. These changes demonstrate a strong understanding of performance optimization techniques within the TensorFlow framework.
pythondata-sciencedeep-learningmlmachine-learning
DeepRec-AI/DeepRec
Sep 2019 - Feb 2020
DeepRec is a high-performance recommendation deep learning framework based on TensorFlow. It is hosted in incubation in LF AI & Data Foundation.
Role in this project:Back-end Developer
Back-end DeveloperContributions:13 commits in 5 months
Contributions summary:Derek primarily focused on optimizing and refactoring core components of the deep learning framework, specifically related to tensor operations and execution efficiency. They optimized the tensor-wrapping of scalar values within the CPU, making it more efficient. Furthermore, the user improved performance in the execution engine by optimizing the FrameState::GetIteration() function, which reduces the time spent under the lock within ExecutorState::PropagateOutputs(). These changes indicate an effort to improve performance in the framework.
pythondeep-learningrecommendationdistributed-trainingmachine-learning
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