Robin
New York, New York, United States
Summary
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SeniorRobin Schmidt is a Senior Machine Learning Research Engineer and manager at Apple with nine years of experience building production-grade ML systems, particularly in machine translation and internationalization across Apple's OS ecosystem. He combines deep research experience from the Max Planck Society—where he published large-scale empirical studies and state-of-the-art domain generalization work—with hands-on engineering contributions to projects like DomainBed and the widely used NL-Augmenter. At Apple he progressed from AI/ML resident to lead roles, shipping model improvements that power translation across iOS, macOS, watchOS and Safari. Comfortable in both research and backend engineering, he has a track record of refactoring and hardening code (adding sanity checks, filter modes, and architecture updates) to make experiments reproducible and robust. Based in New York, he holds advanced computer science degrees from the University of Tübingen and DHBW and brings a pragmatic, measurement-driven approach to bridging novel algorithms and production constraints.
10 years of coding experience
Github Skills (13)
neural-network10
algorithms10
pytorch10
machine-learning10
nlp10
spacy10
python10
natural-language-processing10
resnet9
hyperparameter-tuning9
faster-rcnn8
mask-rcnn8
unit-testing5
Programming languages (5)
Github contributions (5)
GEM-benchmark/NL-Augmenter
Jun 2021 - Jul 2021
NL-Augmenter 🦎 → 🐍 A Collaborative Repository of Natural Language Transformations
Role in this project:
Back-end Developer
Back-end DeveloperContributions:11 reviews, 21 commits, 3 PRs in 13 days
Contributions summary:Robin primarily contributed to implementing and refining filters within the `nl-augmenter` repository. Their work involved creating and modifying filters based on token amounts and speech tags, utilizing the spaCy library for NLP processing. They added a percentage mode to the speech tag filter and integrated default values for the filter parameters. Moreover, the user also introduced a sanity check to validate the filter configurations, enhancing the robustness of the codebase.
nlpinformation-theorysentencedeep-learningmachine-learning
facebookresearch/DomainBed
Sep 2020 - Sep 2020
DomainBed is a suite to test domain generalization algorithms
Role in this project:ML Engineer
ML EngineerContributions:3 reviews, 7 commits, 3 PRs in 5 days
Contributions summary:Robin primarily focused on developing and implementing machine learning algorithms within the domain generalization project. Their contributions include adding a new "RSC" algorithm, cleaning up hyperparameter configurations, and optimizing existing code. They also updated network architectures within the framework and refactored parts of the code, improving overall readability.
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