Jan Pfeifer
Technical Advisor at Optineura AI
Zurich, Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolJan Pfeifer is a Technical Advisor and seasoned machine learning engineer with 14 years of experience applying NLP and ML research to production-scale systems, most recently bridging Google Research advances into real-world products and clients. At Google he led teams and contributed to foundational projects such as TensorFlow GNN and TensorFlow Decision Forests, where he improved training loops, model I/O, and evaluation integration. Now based in Zurich and advising Optineura AI, he applies physics-informed AI to accelerate computational mechanics. His background spans end-to-end systems from statistical machine translation to lattice regression and large-scale decision forest tooling, reflecting a rare mix of research depth and production pragmatism. An early architect on consumer-scale platforms at Yahoo! and an active contributor to influential open-source ML libraries, he combines leadership, hands-on engineering, and a track record of shipping robust, tested ML components.
14 years of coding experience
21 years of employment as a software developer
Computer Engineering, Computer Engineering at Universidade Estadual de CampinasNDO Natural Language Processing, NDO Natural Language Processing at Stanford University
Portuguese, English, Spanish, German, French
Stackoverflow
2,876reputation
278kreached
106answers
12questions
iframe
top-5%
javascript
top-5%
Github Skills (19)
python10
machine-learning10
keras10
tensorflow10
decision-tree10
javascript9
gradient-boosting9
iframe9
unit-testing8
html6
event-listener6
getelementsbytagname6
asp-net6
css6
forms6
Programming languages (15)
Github contributions (5)
tensorflow/decision-forests
May 2021 - Mar 2022
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 2 PRs, 39 comments in 9 months
Contributions summary:Jan made internal changes to the TensorFlow Decision Forests Keras API and core components. Their contributions focused on improving the integration of evaluation steps within the training loops and ensuring proper model saving and loading using the SavedModel format. The user also added tests and modified the code to handle different tasks like classification, regression and ranking, showing focus on enhancements and improvements to the model's functionality.
pythondecision-foreststate-of-the-arttrainingtensorflow
janpfeifer/gonb
Feb 2023 - Mar 2023
GoNB, a Go Notebook Kernel for Jupyter
Contributions:40 releases, 4 reviews, 62 commits in 1 month
data-sciencegogolanggonbjupyter
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