Jiageng Wu
Senior Product Manager at Beijing Frontis
Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolJiageng Wu is a product leader and former software engineer with five years of experience bridging ML engineering, cloud services, and product strategy across Beijing and global teams. Currently a Senior Product Manager at Beijing Frontis, he has driven product efforts after roles at ByteDance, EY, Xiaomi, and hands-on engineering at Lenovo, combining technical execution with strategic insight. Academically grounded with degrees from Peking University, Brandeis, and doctoral study in computational mathematics, he blends rigorous research training with practical product delivery. An active contributor to the prominent Torch-MLIR project, he implemented complex PyTorch-to-MLIR conversion patterns—evidence of deep systems and ML compiler skills that inform his product decisions. Known for translating low-level technical complexity into actionable roadmaps, he seeks roles where applied ML, cloud infrastructure, and product intersect.
5 years of coding experience
2 years of employment as a software developer
Bachelor of Engineering - BE, Internet of Things, Bachelor of Engineering - BE, Internet of Things at 北京邮电大学Doctorate Degree, Computational Mathematics, Doctorate Degree, Computational Mathematics at 吉林大学Master's degree, Computer Science, 3.5, Master's degree, Computer Science, 3.5 at Brandeis University
Stackoverflow
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Github Skills (5)
pytorch10
mlr10
compiler-compiler9
compiler9
machine-learning7
Programming languages (3)
Github contributions (5)
llvm/torch-mlir
Jul 2022 - Sep 2022
The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.
Role in this project:
ML Engineer
ML EngineerContributions:129 reviews, 18 commits, 89 PRs in 2 months
Contributions summary:Jiageng contributed significantly to the Torch-MLIR project, primarily focusing on the conversion of PyTorch operations to the MLIR ecosystem. Their work involved implementing and refining conversion patterns for various PyTorch operations, including those related to MHLO and CHLO dialects. The user's contributions included supporting dynamic shapes, implementing reduce-like and pooling-like op conversions, and adding support for new ops such as `aten.cat` and `aten.clamp`.
pytorchcompilermlir
Vremold/torch-mlir
Jul 2022 - Nov 2024
The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.
Contributions:203 pushes, 62 branches in 2 years 3 months
pytorchmlirtorchecosystemwandb
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