I'm an experienced Julia developer with an appetite for performance challenges.
I'd be extra interested if the work involves any of the following:
- Assessing different parallelization or threading strategies
- Increasing testing coverage to help with maintainability and robustness
- Cleaning up prototype code to prepare for production or publishing as a package
- Contribute to open source dependencies
But I'd also be willing to hop on a codebase that's anywhere from an Elixir microservice to C firmware.
I'm a swift learner, if you get me excited about your product/project, I'm confident I can pick up whatever technologies you use quickly.
Would you guys be interested in someone with pure julia experience, agility in digging into new fields, and vested interest in the topic?
I'm having hard time picking one of the postings, but I think I could be a good fit.
SEEKING WORK | Ontario, Canada | Remote
I'm an experienced Julia developer with a enthusiasm for solving performance and optimization challenges.
I can help if you're looking for Julia expertise to help with any of the following:
- Investigating long compilation or start-up times
- Profiling and identifying performance bottlenecks
- Low-level performance optimization for your simulation code
- Assessing different parallelization or threading strategies
- Increasing testing coverage to help with maintainability and robustness
- Cleaning up prototype code to prepare for production or publishing as a package
- Contribute to a dependency upstream to ease a pain point in your use case
I might also be able to help if you are:
- Rewriting or redesigning from R/MATLAB to Julia/Python/Rust.
- Having performance problems with your Python, Rust, MATLAB, or C code.
- Want to investigate if leveraging GPU compute (CUDA) could be useful for your computations.
I've worked across a wide range of the Julia ecosystem, from high-level tasks such as extracting data from non-compliant csv, and writing custom Plots.jl recipes, to low-level work such as extending the mul! matrix multiplication interface with LAPACK, and investigating type-instabilities and inference problems using SnoopCompile and Cthulhu.
Think I might be able to help? Shoot me an email and let's find out.
I'm an experienced Julia developer with a enthusiasm for solving performance and optimization challenges.
I can help if you're looking for Julia expertise to help with any of the following:
- Investigating long compilation or start-up times
- Profiling and identifying performance bottlenecks
- Low-level performance optimization for your simulation code
- Assessing different parallelization or threading strategies
- Increasing testing coverage to help with maintainability and robustness
- Cleaning up prototype code to prepare for production or publishing as a package
- Contribute to a dependency upstream to ease a pain point in your use case
I might also be able to help if you are:
- Rewriting or redesigning from R/MATLAB to Julia/Python/Rust.
- Having performance problems with your Python, Rust, MATLAB, or C code.
- Want to investigate if leveraging GPU compute (CUDA) could be useful for your computations.
I've worked across a wide range of the Julia ecosystem, from high-level tasks such as extracting data from non-compliant csv, and writing custom Plots.jl recipes, to low-level work such as extending the mul! matrix multiplication interface with LAPACK, and investigating type-instabilities and inference problems using SnoopCompile and Cthulhu.
Think I might be able to help? Shoot me an email and let's find out.
treesitter tech always seemed cool, but never felt I had access to it on my finger tips.
one such tool is https://github.com/Wilfred/difftastic (semantic diff)
After getting used to it with Julia I found it really jarring to go back to plain Jupyter (when I need python) where I have to keep re-executing the cells.
