Srimukh Sripada
Senior Machine Learning Engineer at blackshark.ai
Graz, Styria, Austria
Summary
👤
Senior🎓
Top SchoolSrimukh Sripada is a Senior Machine Learning Engineer with eight years of experience applying computer vision and deep learning to geospatial problems, currently building a 3D digital twin of Earth at Blackshark.ai. He has driven production-ready research at the intersection of satellite/aerial imagery, mapping, and neural nets across companies like TomTom and HERE, shipping vectorized road geometry, fast image similarity search, and camera-pose pipelines. Comfortable across research and engineering, he has boosted real-world products by turning petabyte-scale GPS and vision data into actionable map features and change-detection systems. An active contributor to NumPy’s testing suite, he brings rigorous QA and automation habits often missing in ML codebases. Based in Graz, Austria, he combines hands-on coding curiosity—“bugs are left as an exercise for the reader”—with a practical record of deploying scalable geospatial ML systems.
8 years of coding experience
7 years of employment as a software developer
M.Sc.(Tech), Information Systems, M.Sc.(Tech), Information Systems at BITS PilaniKendriya Vidyalaya Sangathan
English, Hindi, Telugu
Stackoverflow
90reputation
18kreached
5answers
4questions
Github Skills (21)
pytest10
python10
testing10
numpy10
test-automation10
floating-point8
float328
apple-silicon6
tweet6
dictionary6
gil6
bash6
dynamic6
json6
fileoutputstream6
Programming languages (5)
Github contributions (5)
numpy/numpy
Jun 2022 - Jun 2022
The fundamental package for scientific computing with Python.
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:2 reviews, 9 commits, 3 PRs in 8 days
Contributions summary:Srimukh primarily contributed to improving the testing suite of the NumPy library. Their work involved fixing comparison issues for empty structured arrays, handling NaN values correctly in half-precision floats within searchsorted functions, and parameterizing/splitting existing tests for better coverage and maintainability. Furthermore, the user addressed an issue in np.average and np.ma.average functions related to `keepdims` during normalization.
lapackpythonmpindarrayconvolution
postmalloc/blog
Jun 2020 - Aug 2022
Of course it's a blog
Contributions:10 PRs, 28 pushes, 3 branches in 2 years 1 month
jekyll
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