Joyesh Mishra
Senior Software Engineer at Microsoft
Portland, Oregon, United States
Summary
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Senior🎓
Top SchoolJoyesh Mishra is a Senior Software Engineer in Portland with 10+ years building production-grade frameworks and SDKs across Scala, Python and C++, focusing on big data, distributed systems, and ML/DL deployment. He has led engineering efforts at Intel and Microsoft, architecting Spark-based analytics, cloud/hybrid deployment tooling, and neuromorphic computing infrastructure, and contributed backend refactors to the lava neuromorphic framework. Equally comfortable in low-level C++ trading systems and high-level Spark/TensorFlow integrations, he emphasizes test-driven development, practical agile delivery, and fast iteration over drawn-out design theater. A hands-on mentor who prefers shipping working code, he combines research experience in spiking neural networks with pragmatic product engineering to bridge ML research and scalable inference infrastructure.
10 years of coding experience
18 years of employment as a software developer
BE Computer Science and Engineering, BE Computer Science and Engineering at RV College Of EngineeringMasters Computer Science, Masters Computer Science at University of Florida
Stackoverflow
71reputation
3kreached
6answers
1question
Github Skills (9)
compiler-design10
python10
morph10
design-patterns6
data-structures6
move-semantics6
container6
pointer6
mfc6
Programming languages (4)
Github contributions (5)
lava-nc/lava
Sep 2021 - Jan 2023
A Software Framework for Neuromorphic Computing
Role in this project:
Back-end Developer
Back-end DeveloperContributions:55 reviews, 17 commits, 30 PRs in 1 year 3 months
Contributions summary:Joyesh primarily contributed to the refactoring of the message infrastructure within the lava-nc framework. They made changes to the compiler, including the introduction of channel builders and runtime channel builders, and modified the runtime service. Further contributions include the addition of documentation and the integration of pre and post functions during the run, and correcting bugs for network compilation and multiple port polling. These changes indicate a focus on improving the underlying communication and execution flow of the neuromorphic computing framework.
neurosciencepythondeep-learningneural-networksmachine-learning
joyeshmishra/spark-tk
May 2016 - Jan 2017
Contributions:116 pushes, 56 branches, 1 comment in 8 months
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