Janosh Riebesell
Member Of Technical Staff at Periodic Labs
Menlo Park, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJanosh Riebesell is a software engineer with 8 years of experience who blends a strong theoretical physics background (MSc/PhD-level training from Heidelberg and Cambridge) with practical engineering in Python, JavaScript and C/C++. He has worked across academia and industry—from a PhD at Cambridge to roles at Radical AI and Periodic Labs in Menlo Park—focusing on computational chemistry and ML-driven force fields. A prolific open-source contributor, Janosh has improved core data and scientific libraries including pandas, plotly, pymatgen and atomate, often fixing subtle correctness issues and adding robust tests. His work shows a pattern of improving maintainability and developer experience (deprecation fixes, CI/pre-commit, refactors) as much as delivering features. Comfortable across back-end and full-stack tasks, he brings the rigor of theoretical physics to production-grade scientific software. An uncommon combination: he turns advanced ML force-field research into dependable, well-tested code used by the wider materials and data-science communities.
9 years of coding experience
Bachelor of Science (B.Sc.), Physics, Bachelor of Science (B.Sc.), Physics at University of HamburgMaster of Philosophy - MPhil, Artificial Intelligence, Master of Philosophy - MPhil, Artificial Intelligence at University of CambridgeMaster of Science (M.Sc.), Theoretical and Mathematical Physics, Master of Science (M.Sc.), Theoretical and Mathematical Physics at Heidelberg UniversityTheoretical and Mathematical Physics, Theoretical and Mathematical Physics at Kavli Institute for Theoretical Physics, Santa Barbara
English, German, French, Spanish
Stackoverflow
4,172reputation
3.8mreached
22answers
35questions
css
top-5%
responsive-design
top-1%
github
top-5%
font-size
top-5%
markdown
top-5%
Github Skills (54)
unit-testing10
javascript10
sveltekit10
python10
text-manipulation10
testing10
data-science10
css10
responsive-design10
work-flow10
pandas10
workflow-engine10
mongodb-database10
pymatgen10
workflow-automation10
Programming languages (30)
Github contributions (5)
janosh/awesome-normalizing-flows
Dec 2019 - Dec 2022
Awesome resources on normalizing flows.
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 13 reviews, 68 commits in 3 years
Contributions summary:Janosh primarily focused on improving the project's documentation and build process related to the generation of a README file. They implemented a script, `make_readme.py`, that dynamically generates the README from YAML data files. The user refactored the script, added CI badges, and introduced type hints and pre-commit hooks to improve code quality and maintainability. Furthermore, they added the ability to include links to the code repositories in the generated readme file.
flowsbayesian-inferencenormalizingmachine-learningdensity-estimation
janosh/awesome-sveltekit
Apr 2021 - Jan 2023
Awesome examples of SvelteKit in the wild
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:8 reviews, 144 commits, 118 PRs in 1 year 10 months
Contributions summary:Janosh primarily focused on enhancing the user interface and overall functionality of the SvelteKit project. They implemented the index page, added pre-commit hooks, and set up a CI workflow. The user refactored the site's structure, improved the site list component, and replaced PNG screenshots with WebP format. Furthermore, the user integrated Plausible for analytics and developed details pages for each site.
sveltekitsveltesvelte-kit
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