Chenya Zhang
Engineering Leader, Annapurna Trainium AI Chips at Annapurna Labs
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolChenya Zhang is an engineering leader focused on AI hardware acceleration and inference, currently building the Annapurna Trainium AI chips and associated frameworks after leading AI/ML data infrastructure at Apple. With five years of professional experience amplified by earlier roles at LinkedIn and deep academic training in distributed systems (CMU) and NLP (Columbia), Chenya blends systems-level thinking with ML deployment expertise. She is an active Apache YuniKorn committer and PMC member, contributing notable scheduler metrics and queue-level telemetry to a widely used open-source resource scheduler. Known for bridging cloud-native resource scheduling, real-time/offline compute engines, and hardware-aware optimization, she brings a pragmatic approach to squeezing performance from both software stacks and custom AI silicon.
5 years of coding experience
8 years of employment as a software developer
Master's degree Applied Linguistics Natural Language Processing, Master's degree Applied Linguistics Natural Language Processing at Columbia UniversityMaster of Science - MS School of Computer Science. Focus: Distributed System Cloud Computing, Master of Science - MS School of Computer Science. Focus: Distributed System Cloud Computing at Carnegie Mellon University
English, Chinese
Github Skills (7)
kubernetes10
go10
prometheus10
kubernetes-pods10
metric10
microservices8
microservices-application8
Programming languages (6)
Github contributions (5)
apache/yunikorn-core
May 2021 - Dec 2021
Apache YuniKorn Core
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 reviews, 8 commits, 9 PRs in 7 months
Contributions summary:Chenya primarily focused on implementing and refactoring metrics within the Apache YuniKorn core scheduler. They introduced new metrics related to container allocation, application submissions, and application status, and refactored existing metrics for improved consistency and readability. Their work also included refactoring the application submission metrics and adding queue-level metrics, demonstrating a strong understanding of the scheduler's inner workings and its integration with other components. Furthermore, the user made changes to the core scheduler and queue-level metric definitions.
universal-resource-scheduleryunikornapacheapache-yarnkubernetes
chenya-zhang/incubator-yunikorn-core
May 2021 - Jan 2022
Apache YuniKorn Core
Contributions:3 releases, 11 pushes, 8 branches in 8 months
yunikornapache
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