Jeremiah Harmsen
Principal Engineer at Google DeepMind
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolJeremiah Harmsen is a Principal Engineer and longtime Google engineer (since 2005) now leading curiosity-driven research teams across Zurich, Berlin and Amsterdam at Google DeepMind, bridging computer vision, neural radiance fields, ML systems and weather prediction. He founded TensorFlow Hub and TensorFlow Serving and launched the in-house Machine Learning Ninja rotation, combining deep research leadership with practical infrastructure that accelerates model development and deployment. His open-source contributions to tensorflow/models and tensorflow/serving span training and evaluation loops, dataset tooling, per-token NER networks and production-focused refinements such as migrating class descriptions into the computation graph for hermeticity. Trained with multiple advanced degrees including a Ph.D. in electrical engineering from RPI, he pairs rigorous academic roots with hands-on system-building, and outside work can often be found playing volleyball in Zurich with his wife and two children.
12 years of coding experience
11 years of employment as a software developer
Ph.D. Electrical Engineering, Ph.D. Electrical Engineering at Rensselaer Polytechnic Institute
English
Stackoverflow
1reputation
0reached
2answers
0questions
Github Skills (15)
keras10
machine-learning10
deep-learning10
tensorflow10
serve10
python10
bert9
modeling9
trainings9
neural-network9
nlp9
callback8
cpp7
android6
tensorflow-serving6
Programming languages (3)
Github contributions (5)
tensorflow/serving
Feb 2016 - Jul 2016
A flexible, high-performance serving system for machine learning models
Role in this project:
ML Engineer
ML EngineerContributions:19 commits, 11 PRs, 8 pushes in 4 months
Contributions summary:Jeremiah focused on improving the Inception model within the TensorFlow Serving system. Their work involved migrating class description translation into the TensorFlow graph for better model hermeticity. They also updated the Inception description lookup code to correctly handle the background class and fixed a typo. This indicates a focus on model integration and refinement within the serving environment.
cpppythonservingdeep-learningml
tensorflow/models
Apr 2020 - Jul 2020
Models and examples built with TensorFlow
Role in this project:ML Engineer
ML EngineerContributions:8 commits in 3 months
Contributions summary:Jeremiah focused on enhancing the model training and evaluation processes within the TensorFlow models repository. Their commits included modifications to the training loop, enabling custom callbacks, and adding functionalities for sub-model checkpointing and final evaluation. Furthermore, they contributed to data processing tools by introducing dataset module import capabilities. Their work also involved adding a new network for per-token classification tasks, extending the model's capabilities for named entity recognition.
deep-learningtensorflow
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