Fabian Boemer
Staff Cryptography Engineer Manager at Apple
Cupertino, California, United States
Summary
🤩
Rockstar🎓
Top SchoolFabian Boemer is a Staff Cryptography Engineer Manager at Apple with a decade of experience building privacy-preserving machine learning and cryptographic systems. He combines hands-on backend engineering—contributions to high-profile open-source projects like Microsoft SEAL and PlaidML—with technical leadership shaping ML cryptography at scale. His work spans homomorphic encryption optimizations, numerical-precision engineering, and enabling support for challenging data types such as uint64 tensors, reflecting deep practical knowledge of low-level math and performance tradeoffs. Prior roles at Intel as a research scientist and ML technical lead and advanced degrees from Stanford and Caltech inform his ability to translate research into production-ready code. He’s notable for improving NTT performance via Intel HEXL integration and for thoughtful test automation that tightened numerical correctness across frameworks. Based in Cupertino, he blends research rigor with pragmatic engineering to deliver auditable, privacy-first ML infrastructure.
10 years of coding experience
7 years of employment as a software developer
California Institute of TechnologyMaster's degree Computational and Mathematical Engineering, Master's degree Computational and Mathematical Engineering at Stanford University
Github Skills (20)
c-language10
datatypes10
operation10
testing10
tensorrt10
graph10
machine-learning10
constant-folding10
tensorflow10
cryptography10
tensor10
cprogramming-language10
homomorphic-encryption10
optimization10
mlr9
Programming languages (8)
Github contributions (5)
microsoft/SEAL
Feb 2021 - Nov 2021
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 reviews, 24 commits, 11 PRs in 8 months
Contributions summary:Fabian primarily focused on code improvements, including fixing typos, refactoring code, and adding/modifying documentation. They also integrated Intel HEXL for NTT computation, improving performance. Further contributions included optimizing CKKS multiplication, refining the CKKS multiplication and, and addressing issues related to shared builds and memory management, demonstrating a solid understanding of the codebase.
encryption-librarymicrosoftfhesecurersa
NervanaSystems/ngraph
Dec 2018 - Feb 2020
nGraph - open source C++ library, compiler and runtime for Deep Learning
Role in this project:Back-end Developer
Back-end DeveloperContributions:15 commits, 14 PRs, 18 pushes in 1 year 2 months
Contributions summary:Fabian's contributions primarily focused on enhancing the nGraph library's functionality and stability. This involved implementing new features such as square root calculations within constant folding, improving the serializer's compatibility with newer APIs, and fixing compiler errors. Furthermore, the user addressed code quality issues by removing redundant header includes and adding annotations. These changes contribute to improvements in compilation and overall performance of the library.
inference-enginecppc-librarydeep-learningtvm
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