Jacob Prince-bieker
Founding Machine Learning Engineer at TipplyAI
City of Edinburgh, Scotland, United Kingdom
Summary
👤
Senior🎓
Top SchoolJacob Prince-bieker is a founding machine learning engineer with 11 years of experience translating research ideas and massive interdisciplinary datasets into production ML systems, currently building world models for extreme weather intelligence. He has led ML research and engineering across climate tech, astronomy, and healthcare—designing large-scale multi-modal data pipelines, data assimilation systems, and rapid-refresh weather models at startups like TipplyAI, Secondlaw, VIDA, and Open Climate Fix. Comfortable from backend I/O (contributions to the high-performance webdataset library) to supercomputing visualizations, Jacob blends deep domain knowledge in astrophysics and observational data with practical productionization skills on AWS and PyTorch. He is known for mentoring teams and shipping reproducible forecasting and simulation pipelines that bridge research and operations. Based in Edinburgh, he brings a habit of connecting seemingly disparate fields—gamma-ray optics to satellite imagery—to unlock novel ML applications for climate and science.
11 years of coding experience
10 years of employment as a software developer
Bachelor's degree, Physics, Computer And Information Science, Bachelor's degree, Physics, Computer And Information Science at University of OregonAstronomy and Computer Science, Astronomy and Computer Science at Aarhus UniversityMasters Astronomy and Data Science, Astronomy and Astrophysics, Masters Astronomy and Data Science, Astronomy and Astrophysics at Leiden UniversityHighschoel Diploma, Highschoel Diploma at La Salle Catholic College Preparatory
English, German
Stackoverflow
1reputation
0reached
0answers
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Github Skills (8)
data-handling10
encode10
pytorch10
decode10
python10
encoding10
decoding10
numpy10
Programming languages (8)
Github contributions (5)
webdataset/webdataset
May 2021 - Jun 2021
A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5 commits, 1 PR, 2 comments in 7 days
Contributions summary:Jacob primarily contributed to the backend functionality of the `webdataset` repository, focusing on data handling and processing. Their work involved implementing a Numpy encoder and fixing auto-decoding for various file extensions, including those related to PyTorch and NumPy. The contributions included modifying the `writer.py` and `autodecode.py` files to enhance the data processing capabilities. Additionally, the user made updates to the codebase to correct formatting and merge branches.
pytorchpythondeep-learningproblemsmachine-learning
openclimatefix/metnet
Sep 2021 - Dec 2022
PyTorch Implementation of Google Research's MetNet and MetNet-2
Contributions:6 releases, 5 reviews, 100 commits in 1 year 3 months
deep-learningpytorchtorchvisionpytorch-implementation
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