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Builders & Technical Leaders
Philipp Schmid
Staff Engineer, Developer Experience |Google DeepMind
Former Technical Lead at Hugging Face who built Inference Endpoints and scaled cloud partnerships to ~$100M revenue. Now leading AI DevRel/DevX at Google DeepMind. First German AWS ML Hero. Prolific open-source contributor and technical writer on LLM deployment, AI agents, and MCP.
Biography
Deep Research
Philipp Schmid is a Staff Engineer for Developer Experience and Developer Relations at Google DeepMind, building the first AI DevRel/DevX team to bring Google DeepMind's research to every developer. Previously he was Technical Lead at Hugging Face for four years (2021-2025), where he led strategic partnerships with AWS, Google Cloud, Azure, Nvidia, Dell, Cloudflare, and DigitalOcean, helping grow revenue from $0 to ~$100 million through cloud and hardware offerings. He created Hugging Face Inference Endpoints and the SageMaker Hugging Face Inference Toolkit, and collaborated on projects including Zephyr, SmolLM, and StarCoder. He also created IGEL, the first German instruction-tuned Large Language Model. Recognized as the first German AWS Machine Learning Hero in 2021, Schmid is a prolific technical writer with hundreds of blog posts, tutorials, and open-source projects covering LLM fine-tuning, deployment, AI agents, and the Model Context Protocol (MCP).
LLM Deployment & InferenceAI Agents & MCPGemini & Gemma Developer ExperienceHugging Face TransformersAWS SageMaker ML InfrastructureRLHF & LLM Fine-tuningOpen-Source AI ToolingModel Context Protocol (MCP)Cloud-Native AI ArchitectureDeveloper Relations & DevX
Timeline
21 Research21 total
2026
2026-01Research
Published 'Gemini Interactions API Quick Start' guide2026-02Research
Created ia-agents repository on GitHub (25 stars)2026-02Research
Created self-learning-skill repository on GitHub (58 stars)2025
2025-01Research
Announced departure from Hugging Face after 4 years, having grown the Hub from ~5,000 models to millions of developers2025-01Research
Published 'How to use Anthropic MCP Server with open LLMs, OpenAI or Google Gemini' tutorial2025-02Research
Created gemini-samples repository — personal samples and guides for Gemini models (1,357 stars)2025-03Research
Joined Google DeepMind as Staff Engineer for Developer Experience and Developer Relations2025-07Research
Released gemini-cli-extension — collection of Gemini CLI extensions and commands (148 stars)2023
2022
2021
Key Contributions
Hugging Face Inference Endpoints
Created and led as Technical Lead the secure production solution for deploying ML models on dedicated, fully managed infrastructure at Hugging Face
gemini-samples
Personal samples, snippets, and guides showcasing experiments and implementations using Google DeepMind Gemini models. 1,357 stars on GitHub.
mcp-cli
Lightweight CLI to interact with MCP servers, making it easy to test and debug Model Context Protocol integrations. 1,028 stars on GitHub.
Deep Learning with PyTorch and Hugging Face
Comprehensive tutorial collection for getting started with Deep Learning using PyTorch and Hugging Face libraries. 1,360 stars.
SageMaker Hugging Face Inference Toolkit
Open-source library for serving Hugging Face Transformers models on Amazon SageMaker, enabling enterprise LLM deployment
Clipper.js
HTML to Markdown converter and crawler for web content extraction. 616 stars on GitHub.
IGEL
The first German instruction-tuned Large Language Model, capable of sentiment analysis, translation, and other NLP tasks in German
AI Agent Benchmark Compendium
Compendium of over 50 benchmarks for evaluating AI agents across function calling, tool use, reasoning, coding, and computer interaction. 112 stars.
EasyLLM
Helpful tools and utilities for working with large language models, simplifying common LLM workflows. 474 stars.
Notable Quotes
“
Every good story has to end, and after 4 incredible years at Hugging Face, it's time for me to start my next adventure. When I joined Hugging Face, we were a small team of 20 and the Hub had less than 5,000 models.
“
It has never been easier to learn how Git works and the Git commands with having LLMs explain you the concepts. It might sound scary now to become a software developer, but I think there never has been a better or easier time to get started.
“
MCP is an open standard that enables developers to build secure agents and complex workflows on top of LLMs. It tries to decouple components and define clear interfaces.
12 sources(click to expand)
Philipp Schmid — Personal Website & Biophilschmid.de — Blogphilschmid on GitHub (247 repos, 3.7K followers)philschmid on Hugging FacePhilipp Schmid — Google DeepMind | LinkedInPhilipp Schmid — Technical Lead at Hugging Face | The OrgDemocratizing AI with Hugging Face — InterviewGoogle DeepMind's AI playbook for engineering — Dev InterruptedBeyond Chatbots: Agentic AI with Gemini — MLOps Community TalkPhilipp Schmid — ProjectsPhilipp Schmid | AWS Machine Learning HeroFarewell to Hugging Face announcement on X
Research generated March 19, 2026
Builders & Technical Leaders/Philipp Schmid
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