Philipp Schmid
Staff Engineer (Developer Experience & Developer Relations)
Nuremberg, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolPhilipp Schmid is a Staff Engineer specializing in Developer Experience and Developer Relations, currently at Google DeepMind, with seven years of hands-on experience building ML tooling and cloud integrations. Previously a Tech Lead at Hugging Face, he led partnerships with AWS, Google, and Azure and helped create Hugging Face Inference Endpoints, blending product-facing advocacy with deep engineering. An active open-source contributor, Philipp has shaped core projects like transformers, optimum, datasets and the SageMaker SDK—adding ONNX/ORT inference support, SageMaker optimizations (including Trainium/inf2), and S3-backed dataset workflows. He excels at moving research-grade models into reliable production paths, from DeepSpeed and PyTorch 2.0 tuning to containerized inference images. Based in Nuremberg, he pairs platform-level architecture skills with developer empathy, often surfacing subtle usability fixes like session/auth handling and improved training/deployment ergonomics.
7 years of coding experience
5 years of employment as a software developer
Bachelor of Science - BS Wirtschaftsinformatik, Bachelor of Science - BS Wirtschaftsinformatik at Baden-Wuerttemberg Cooperative State University (DHBW)
Github Skills (57)
continuous-deployment10
transformers10
pytorch10
filesystem10
docker10
distributed-training10
pytest10
python10
machine-learning-models10
parameter-tuning10
machine-learning10
inference10
onnx10
text-classification10
dockers10
Programming languages (16)
Github contributions (5)
philschmid/deep-learning-pytorch-huggingface
Dec 2022 - Mar 2023
Role in this project:
ML Engineer
ML EngineerContributions:5 reviews, 37 commits, 39 PRs in 3 months
Contributions summary:Philipp's commits primarily revolve around fine-tuning and experimenting with Transformer models for various natural language processing tasks within the Hugging Face ecosystem. The contributions involve setting up environments, loading datasets, tokenizing text, fine-tuning models like BERT and FLAN-T5, and evaluating performance. They also demonstrate applying advanced techniques such as DeepSpeed for large model training and PyTorch 2.0 optimizations to improve training efficiency.
huggingface/notebooks
Mar 2021 - Nov 2022
Notebooks using the Hugging Face libraries 🤗
Role in this project:ML Engineer
ML EngineerContributions:17 reviews, 119 commits, 127 PRs in 1 year 7 months
Contributions summary:Philipp contributed to the development and implementation of sample notebooks focused on using the Hugging Face libraries. The primary focus of the commits was to demonstrate fine-tuning and deployment of machine learning models on the Amazon SageMaker platform, specifically using PyTorch, and text classification tasks. The contributions included example notebooks for different use cases like training and deployment using spot instances.
nlpdeep-learningmachine-learninghugging-facenotebooks
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