Technologies: Python, C++, some TypeScript and Rust, Docker, some Kubernetes, build systems (Make, CMake, Bazel). Familiar with Python CPU/GPU computing libs like NumPy, SciPy, JAX, PyTorch.
Looking for a job in software engineering or research, preferably with focus on ML / scientific computing, since that's what I consider myself good at. I am a regular contributor to open source projects (nicholasjng on GitHub), notably in benchmarking (google/benchmark and aai-institute/nnbench), scientific computing (JAX) and Python<->C++ interop (nanobind).
Ideally, in my next role I want to pick up more Rust, solidify my C++ knowledge, and also work on compiler tech including, but not limited to ML compilers like MLIR, XLA, and Triton. Merged my first MLIR PR a few weeks back, hopefully more to come soon.
I'm a software dev with math/physics background, primarily focused on machine learning and data science/engineering. My main topics of study were theoretical physics, statistics, numerical analysis and ML.
I have worked with some other tools that fit the broader "MLOps" job description too, like Docker/Kubernetes, primarily on GCP. I would like to continue my career in this space (ideally as an MLOps / Data Engineer).
On the side, I have been contributing to a couple of FOSS projects, like Google Benchmark, JAX and ZenML. My latest project is pybm, a Python CLI for reproducibly benchmarking code throughout a git history. You can find it along with several other projects on my github at https://github.com/nicholasjng.
I'm a software dev with math/physics background, primarily focused on machine learning and data science/engineering. I can operate some other tools that fit the broader "MLOps" job description too, like Docker/Kubernetes, and some ML-related products of GCP and AWS. I did some webdev work in training visualizations for ML algorithms and for my personal website.
My main topics of study were statistics, ML, and analysis; I am fit in theoretical ML/DL concepts and numerical programming, allowing me to develop and implement performant algorithms.
On the side, I have been contributing to a couple of OSS projects, e.g. Google Benchmark and JAX. My latest project is pybm, a Python CLI for reproducibly benchmarking code throughout a git history. You can find it along with several other smaller projects in my github profile at https://github.com/nicholasjng.
I am a physics/math grad turned software developer with about 3 years of experience, looking for a new full-time job. I have sound knowledge in Numerical Computing, Statistics and Machine Learning, along with practical experience in ML Engineering and Data Science. I am most familiar with Python as that's been my day job for the last 3 years, but I have C++ experience as well.
I post my personal projects on my Github at https://github.com/nicholasjng . Also, I started contributing to some open source projects like JAX and Google Benchmark in the past year.
In that regard, my main focus has been my hobby project pybm, a CLI for easily benchmarking and comparing Python code performance between different reference points in a git history with only three commands. (Also the reason why I felt confident listing git under my "technologies" :))
Remote: yes
Willing to relocate: yes
Technologies: Python, C++, some TypeScript and Rust, Docker, some Kubernetes, build systems (Make, CMake, Bazel). Familiar with Python CPU/GPU computing libs like NumPy, SciPy, JAX, PyTorch.
Résumé/CV: https://www.linkedin.com/in/nicholas-junge
Email: nicho [dot] junge [at] gmail.com
Looking for a job in software engineering or research, preferably with focus on ML / scientific computing, since that's what I consider myself good at. I am a regular contributor to open source projects (nicholasjng on GitHub), notably in benchmarking (google/benchmark and aai-institute/nnbench), scientific computing (JAX) and Python<->C++ interop (nanobind). Ideally, in my next role I want to pick up more Rust, solidify my C++ knowledge, and also work on compiler tech including, but not limited to ML compilers like MLIR, XLA, and Triton. Merged my first MLIR PR a few weeks back, hopefully more to come soon.