Gabe Joseph
Open-Source Software Engineer at Coiled
Santa Fe, New Mexico, United States
Summary
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Rockstar🎓
Top SchoolGabe Joseph is an open-source software engineer with 11 years of experience focused on making Earth-observation and geospatial data broadly accessible. Based in Santa Fe, he contributes core functionality to Dask at Coiled, improving distributed scheduling, testing, and performance for one of Python’s most important parallel computing projects. Previously he led the team at Descartes Labs building a geospatial computation engine for scientists, and has a track record of turning hard-to-reach field data (bioacoustics, Denali recordings) into science-ready, reproducible datasets. Gabe combines backend and DevOps expertise with field-hardened pragmatism—he’s as comfortable optimizing task scheduling in dask/distributed as he is deploying sensors in remote Alaska. He’s driven by enabling researchers to ask new questions with data, not just serving those who already know GDAL.
11 years of coding experience
6 years of employment as a software developer
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Tufts University
Spanish
Stackoverflow
81reputation
2kreached
3answers
0questions
Github Skills (28)
scheduled-tasks10
pytest10
python10
data-manipulation10
scheduler10
distributed-computing10
numpy10
scheduling10
sched10
dask10
parallel-computing10
task-scheduler10
job-scheduling10
data-analysis10
pandas9
Programming languages (11)
Github contributions (5)
dask/distributed
Jun 2021 - Dec 2022
A distributed task scheduler for Dask
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:576 reviews, 72 commits, 180 PRs in 1 year 5 months
Contributions summary:Gabe contributed significantly to the `dask/distributed` repository by implementing new features and improving existing ones. Key contributions include supporting fixtures and parametrization with `gen_cluster`, as well as co-assigning root-ish tasks to reduce future data transfer. The user also addressed several bugs, enhanced code quality, and updated documentation. Additionally, the user made code changes that related to the testing, scheduling, and overall performance improvements of the Dask distributed system.
pythonschedulerdaskdistributed-computingdistributed-systems
dask/dask
Mar 2017 - Apr 2023
Parallel computing with task scheduling
Role in this project:Back-end Developer
Back-end DeveloperContributions:208 reviews, 46 PRs, 2 pushes in 6 years 2 months
Contributions summary:Gabe contributed to the dask/dask repository by addressing a variety of issues, including handling array-like non-arrays in the `random.choice` function and improving the handling of masked elements in the moment calculation. They made changes to core routines, addressing the need for support of lazy values for `range/bins` in histograms and improved the functionality of `asanyarray` to handle sequences containing dask arrays. Additionally, the user made updates to documentation and fixed issues regarding NEP18 dispatching and the number of outputs in `apply_gufunc`.
pythonschedulingparallelnumpydask
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