Martin Maas
Senior Staff Research Scientist at Google DeepMind
Mountain View, California, United States
Summary
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Senior🎓
Top SchoolMartin Maas is a Senior Staff Research Scientist at Google DeepMind with 11 years of experience bridging hardware, operating systems, and managed language runtimes. He applies machine learning to reimagine systems design from first principles, with deployed work across Google Compute Engine, TCMalloc, Pixel phones and the TPU compiler. A UC Berkeley PhD and Cambridge alumnus who represented Germany at IOI and ISEF, he combines deep theoretical training with hands-on systems engineering. His open-source contributions to google/tcmalloc—adding telemetry and refactoring lifetime and clock tracking—reflect a focus on observability and memory-management robustness at production scale. Colocated in Mountain View, he’s interested in the full stack and in how AI can be integrated into system architectures rather than bolted on.
12 years of coding experience
12 years of employment as a software developer
MS/PhD, Computer Science, MS/PhD, Computer Science at University of California, BerkeleyBA, Computer Science, BA, Computer Science at University of Cambridgee-Juniors 2006/2007
German, English
Github Skills (16)
performance-tuning10
memory-management10
performance-monitor10
c-language10
performance-analysis10
performance-analytics10
performance-monitoring10
cprogramming-language10
performance-measurement10
data-structure9
algorithm9
data-structures9
algorithms9
time-series8
data-analysis8
Programming languages (8)
Github contributions (5)
google/tcmalloc
Feb 2020 - Feb 2022
Role in this project:
Back-end Developer
Back-end DeveloperContributions:21 commits in 2 years
Contributions summary:Martin made multiple contributions focused on refactoring and extending the time series tracking functionality within the TCMalloc library. Their work involved the creation of new telemetry features and the refactoring of existing code to eliminate duplication and improve maintainability. The user also implemented additional telemetry for the `HugePageFiller` to capture metrics related to page usage and fragmentation over time, contributing to a deeper understanding of memory management. Further contributions include refactoring and optimization related to the clock and object lifetime tracking within TCMalloc.
google-research/ml-for-systems-taxonomy
Jul 2020 - Mar 2021
Contributions:10 commits, 1 push, 1 branch in 8 months
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