Rolf Jagerman
Senior Software Engineer at Google DeepMind
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolRolf Jagerman is a Senior Software Engineer at Google DeepMind with 12 years of experience building ML infrastructure and applied research for large-scale products. He specializes in Large Language Models, Learning-to-Rank and counterfactual learning, and leads development of RAX, an open-source JAX library for ranking systems. His work spans academic impact—20+ publications at NeurIPS, KDD, SIGIR and others—and product impact, contributing to launches across YouTube, Google Cloud AI and Chrome Web Store. Previously a founder and frequent open-source contributor (notably extending TensorFlow Datasets’ LibSVM ranking parser), he blends deep research rigor from a PhD at the University of Amsterdam with pragmatic engineering at Google and Apple. Colocated in New York, he combines library-building and production deployments, often turning ranking research into robust, reusable tooling.
12 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of AmsterdamMaster’s Degree, Computer Science, Master’s Degree, Computer Science at ETH ZurichBachelor, Computer Science, Bachelor, Computer Science at Delft University of Technology
English, Dutch
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (9)
data-set10
machine-learning10
tensorflow10
python10
data-model10
numpy10
user-data10
datasets10
jax8
Programming languages (4)
Github contributions (5)
tensorflow/datasets
Sep 2021 - Jan 2023
TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
Role in this project:
ML Engineer
ML EngineerContributions:7 commits in 1 year 3 months
Contributions summary:Rolf's primary contribution involves implementing and expanding a LibSVM ranking parser for use with various datasets. This includes adding support for new datasets like Istella, MSLR-WEB, and Yahoo LTRC, as well as refactoring the parser to bundle features into a single 'float_features' feature, and adding query and document identifiers. These changes directly support the use of these datasets within the TensorFlow Datasets framework for machine learning tasks.
jaxtensorflowmachine-learningdatadatasets
rjagerman/scalarank
Nov 2016 - Jun 2020
Contributions:32 commits, 4 PRs, 13 pushes in 3 years 7 months
learning-to-rankscala-libraryscala
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