Peter Schafhalter
Member Of Technical Staff at Microsoft AI
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolPeter Schafhalter is a Member of Technical Staff at Microsoft AI and a PhD-trained distributed systems and ML engineer with a decade of experience building high-performance back-end systems. His work at UC Berkeley RISE Lab and on the Ray project focused on optimizing core data structures and object transfer for large-scale AI workloads, demonstrating a deep attention to performance and systems-level efficiency. He has applied that expertise in industry research roles, including a DeepMind internship where he improved language model specialization for faster training and better capability retention. Comfortable across cloud computing, autonomous driving stacks, and ML infrastructure, he bridges research and production to scale complex AI systems. Based in the Bay Area, he pairs academic rigor with production impact—shipping code used in real systems and contributing to widely adopted open-source projects. An under-the-radar strength is his habit of refactoring low-level data structures to yield outsized throughput gains, reflecting a pragmatic, systems-first engineering mindset.
10 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of California, Berkeley
English, German, Spanish
Github Skills (8)
data-structures10
ray10
c-language10
cprogramming-language10
performance-optimization10
data-structure10
redis9
distributed-systems9
Programming languages (7)
Github contributions (5)
ray-project/ray
Sep 2017 - Oct 2019
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:53 commits, 65 PRs, 7 pushes in 2 years 1 month
Contributions summary:Peter primarily contributed to the backend of the Ray project, focusing on data structure updates and optimization. Their commits involved refactoring data structures used by the plasma manager, including converting UT strings to std::string and UT arrays to std::vector. They also implemented and refactored data structures for caching and wait requests within the plasma manager, demonstrating a focus on performance and efficiency. Additionally, the user implemented new data structures within the core system for object transfer.
pythonconsistsruntimetensorflowserving
pschafhalter/erdos
Nov 2018 - Aug 2022
Contributions:4 PRs, 277 pushes, 163 branches in 3 years 10 months
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