Karla Saur
Member Of Technical Staff at Anthropic
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolKarla Saur is a distributed systems engineer with 11 years of experience building and optimizing cloud-native infrastructure for high-availability, large-scale workloads across Kubernetes, serverless, and multi-cloud environments. She has driven capacity efficiency and AI training/inference platform work at Anthropic and NVIDIA, and led infrastructure and workload optimizations at Microsoft Azure where she co-created Hummingbird to run traditional ML models on GPUs. Her background spans research-grade systems (PhD work on dynamic software updates) to production upgrades at global scale, including live Docker rollouts across 15 regions. Comfortable prototyping in Python, C, and Go, she pairs rigorous experimentation with production-hardened engineering and a particular interest in VNFs and containerized ML workflows. Based in Seattle, she brings a blend of academic depth and hands-on operational experience—often surfacing subtle deployment gaps before they become customer-facing incidents.
11 years of coding experience
13 years of employment as a software developer
Johns Hopkins UniversityDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at University of MarylandB.S., Computer Science, Mathematics, B.S., Computer Science, Mathematics at University of Iowa
Github Skills (12)
scikit10
pytorch10
xgboost10
machine-learning10
onnx10
python10
lightgbm10
scikit-learn10
testing9
neural-network8
artificial-neural-networks8
pytest7
Programming languages (9)
Github contributions (5)
microsoft/hummingbird
Mar 2020 - Jan 2023
Hummingbird compiles trained ML models into tensor computation for faster inference.
Role in this project:
ML Engineer
ML EngineerContributions:19 releases, 195 reviews, 321 commits in 2 years 10 months
Contributions summary:Karla was actively involved in developing and testing machine learning models within the `microsoft/hummingbird` repository. Their contributions focused on implementing and refining tests for various machine learning algorithms from scikit-learn, including Random Forest, XGBoost, and LightGBM. The user also addressed bugs and performed refactoring to improve code quality and test coverage. Additionally, they worked on integrating these models with different backends, such as PyTorch and ONNX.
pytorchcomputationml-modelspythondeep-learning
microsoft/vasim
Sep 2024 - Sep 2024
Contributions:36 reviews, 32 PRs, 93 pushes in 22 days
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