Nicholas Junge
Senior MLOps Engineer at appliedAI Institute for Europe gGmbH
Munich, Bavaria, Germany
Summary
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Rockstar🎓
Top SchoolNicholas Junge is a Senior MLOps Engineer based in Munich with six years of experience building reproducible, production-ready ML tooling grounded in a strong math and physics background. He specializes in experiment reproducibility, build automation, and sequence-focused machine learning, having modernized build pipelines (including cross-compilation and Python 3.12 support) for notable projects like google/benchmark and contributed robustness improvements to JAX. Nicholas combines hands-on engineering—from Bazel and GitHub Actions automation to pandas/SQL data pipelines—with experience training VAEs and deploying real-time ML web apps. He has a track record of raising code quality (test coverage and future-proofing toolchains) and translating numerical methods into practical systems for industry and research. Outside work he documents experiments and thoughts on his personal blog and pursues photography and languages, signaling a detail-oriented, curious mindset.
6 years of coding experience
2 years of employment as a software developer
Bachelor of Science (B.Sc) Physics, Bachelor of Science (B.Sc) Physics at Bielefeld UniversityMaster of Science (M.Sc) Mathematics, Master of Science (M.Sc) Mathematics at Technical University of Munich
Norwegian, Danish, German, English, Swedish, Spanish
Github Skills (15)
github-ci10
bit10
jax10
githubaction-workflow10
python10
bazel10
wheels10
build-automation10
cicd10
numpy10
bitwise10
linear-algebra9
crossbuild9
cross-compiling9
unit-test8
Programming languages (11)
Github contributions (5)
google/benchmark
Jul 2021 - Dec 2022
A microbenchmark support library
Role in this project:
DevOps Engineer & Build Automation Engineer
DevOps Engineer & Build Automation EngineerContributions:25 reviews, 15 commits, 51 PRs in 1 year 5 months
Contributions summary:Nicholas primarily focused on improving the build and deployment process for the Google Benchmark library, specifically related to Python bindings. Their contributions include fixing build issues, especially on Windows, related to dependencies and toolchain compatibility. They implemented automated build steps using Bazel and GitHub Actions, including cross-compilation support for macOS ARM builds. Furthermore, the user modernized the build process, introducing features such as dynamic versioning, PEP518 compliance, and support for Python 3.12, with a focus on future-proofing the build infrastructure.
cppbenchmarkingbazelsupport-librarymicrobenchmark
jax-ml/jax
May 2021 - Oct 2022
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Role in this project:Back-end Developer
Back-end DeveloperContributions:44 reviews, 13 commits, 15 PRs in 1 year 5 months
Contributions summary:Nicholas primarily contributed to the implementation of features within the JAX library, specifically focusing on the `jax.numpy` module. Their work involved adding support for `jnp.r_` and `jnp.c_` functionalities, incorporating auxiliary data support for custom linear solves and custom roots. The user also addressed concretization errors in jnp indexing routines, improving the library's robustness. Additionally, the user contributed by adding bitwise XOR reducer to `lax.reduce` and enabling it for integer dtypes.
pytorchpythonjitautomatic-differentiationgpu
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