Daniel Crankshaw
Member Of Technical Staff at Microsoft AI
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDaniel Crankshaw is a seasoned software engineer with 15 years of experience building scalable, low-latency systems and applied AI infrastructure across top-tier tech labs. He holds a PhD in Computer Science from UC Berkeley and has moved between research and product roles at Microsoft and Google, now serving as Member of Technical Staff at Microsoft AI. Daniel’s work spans core backend systems, exemplified by contributions to the widely used ucbrise/clipper project where he implemented a query processing API and selection-policy framework for production prediction serving. Known for resolving tough integration and compilation challenges, he brings deep systems-level expertise in C++ and distributed serving architectures. Colleagues rely on him to bridge research ideas and production code, translating novel algorithms into robust, maintainable platforms. Based in San Francisco, he pairs academic rigor with pragmatic engineering to deliver high-impact infrastructure for machine learning at scale.
15 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of California, BerkeleyJohns Hopkins University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (19)
algorithm10
algorithms10
graph-algorithms10
spark10
c-language10
big-data10
html-template10
python-templates10
data-structure10
scala10
code-templates10
apptemplates10
data-structures10
cpp-templates10
cprogramming-language10
Programming languages (8)
Github contributions (5)
ucbrise/clipper
Oct 2016 - Jun 2018
A low-latency prediction-serving system
Role in this project:
Back-end Developer
Back-end DeveloperContributions:4 releases, 118 commits, 259 PRs in 1 year 7 months
Contributions summary:Daniel's primary contributions centered around the implementation of a Query Processing API. This involved the development of a framework for selection policies, including the use of templates and polymorphism. The user focused on establishing the core structure, integrating necessary libraries like Boost, and resolving initial compilation issues. The changes involved significant modifications to the core data structures and API for a prediction-serving system.
pythonservingpredictiondeep-learninglatency
dcrankshaw/vrouter
Mar 2011 - Apr 2011
Contributions:48 commits in 17 days
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