Magnus Stensmo

San Francisco, California, United States

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Summary

🤩

Rockstar

🎓

Top School

Magnus Stensmo is a machine learning and NLP engineer with a decade of experience turning research prototypes into production-grade systems across information retrieval, text mining, recommendation, and deep learning. Based in San Francisco, he bridges academic rigor (PhD and MSE from KTH) with hands-on engineering, shipping text and model-integration work in prominent open-source projects like H2O.ai’s h2o-3. He’s comfortable across the full data science stack—from text encoding and one-hot conversions to model optimization and unit testing—bringing both scientist and full-stack practitioner perspectives. Known for tackling unstructured knowledge and scalable ML pipelines, he combines attention to testing and configuration detail with a knack for moving complex NLP solutions into reliable production.

10 years of coding experience

MSE, Computer Science and Engineering, MSE, Computer Science and Engineering at KTH Royal Institute of Technology

Github Skills (11)

machine-learning10

deep-learning10

text-encoding10

python10

mxnet9

tensorflow9

unit-testing8

data-science8

javas6

java6

automl4

Programming languages (1)

Jupyter Notebook

Github contributions (5)

h2oai/h2o-3

Sep 2016 - May 2017

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Role in this project:

ML Engineer

Contributions:93 commits, 15 PRs, 52 pushes in 8 months

Contributions summary:Magnus primarily contributed to the `hex.deepwater` module within the `h2o-3` repository, focusing on text processing and model integration. Their work involved adding unit tests for converting text to one-hot arrays and developing text encoding functionalities for the Deep Water framework. They also made adjustments to parameters and configurations for Deep Water, suggesting a focus on model optimization and testing, particularly concerning image and text processing pipelines.

xgboostgampythonk-meansautoencoders

h2oai/deepwater

Aug 2016 - Nov 2017

Contributions:126 commits, 4 PRs, 107 pushes in 1 year 2 months

h2odeep-learninggpubackendsmachine-learning

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