Junjie Qu
Senior Technical Account Manager at Amazon Web Services (AWS)
Küssnacht (SZ), Schwyz, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolJunjie Qu is a Senior Technical Account Manager at AWS with nine years of software engineering experience and a decade-plus background in architecture and team leadership. He blends deep hands-on skills in C#, Java, cloud-native AWS architectures, and Linux-based systems with strong practices in TDD, DDD, and DevOps to deliver resilient, scalable solutions. Junjie has led cross-functional teams and projects from embedded desktop apps to microservices and serverless systems, and he coaches teams on process, planning, and engineering excellence. He contributes to notable open-source work—helping improve Facebook Research’s faiss library for GPU indexing and performance-critical bug fixes—demonstrating ML-relevant systems expertise beyond his day-to-day cloud role. Comfortable across databases, containers, IaC and CI/CD, he still pursues newer languages like Rust and Go by learning through doing. Based in Switzerland, he combines a formal MSc in Software Engineering with a lifelong passion for programming and pragmatic problem-solving.
9 years of coding experience
14 years of employment as a software developer
Bachelor of Engineering (B.E.) Computer Software Engineering, Bachelor of Engineering (B.E.) Computer Software Engineering at Shandong UniversityMaster of Science (M.Sc.) Computer Software Engineering, Master of Science (M.Sc.) Computer Software Engineering at University of Limerick
English, chinese- mandarin, German
Github Skills (14)
similarity-search10
machine-learning10
c-language10
cprogramming-language10
indexing10
linear-algebra10
python9
gpgpu9
gpu9
performance-optimization9
cuda9
gpu-programming9
testing8
pytest8
Programming languages (5)
Github contributions (5)
facebookresearch/faiss
Feb 2024 - Apr 2026
A library for efficient similarity search and clustering of dense vectors.
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 16 reviews, 57 PRs in 2 years 2 months
Contributions summary:Junjie contributed to the development and improvement of the faiss library, focusing on efficient similarity search and clustering of dense vectors. Their work included preparing for and releasing new versions, fixing critical bugs in the core functionalities of the library, like decoding in IVFPQFastScan and implementing reconstruct_n for GPU-based IVFFlat indexes. They also focused on optimizing performance by replacing deprecated functions and fixing Swig builds for various environments.
clusteringsimilarity-search
njwhite/faiss
Jul 2024 - Aug 2024
A library for efficient similarity search and clustering of dense vectors.
Contributions:3 pushes in 22 days
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