Daniel Grijalva
Software Engineer at Improving
Sonora, Mexico
Summary
👤
Senior🎓
Top SchoolDaniel Grijalva is a software engineer with 11 years of experience specializing in Python and full-stack development, currently modernizing a production codebase and improving reliability at Improving. He combines backend expertise (Django, Flask, APIs, Celery) with front-end skills in React and UI/UX, and has boosted test coverage and proven concurrency with Locust-based load tests. A frequent open-source contributor, Daniel improved user interaction for the popular textgenrnn library and solved algorithmic challenges in Google Code Jam repos, demonstrating both ML tooling and algorithmic problem-solving. He’s comfortable across the stack—from PySpark analytics in Jupyter to CI/CD and cloud incident resolution—and mentors teammates to accelerate onboarding and productivity. Based in Sonora, Mexico, he brings practical experience shipping customer-facing dashboards and mobile-ready web apps while keeping a strong interest in data science and interactive user experiences.
11 years of coding experience
3 years of employment as a software developer
Engineer’s Degree, Computer Software Engineering, Engineer’s Degree, Computer Software Engineering at Universidad de Sonora
English, Spanish
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (15)
text-generation10
javas10
solve10
keras10
algorithms10
deep-learning10
tensorflow10
python10
java10
solution10
scala10
nlp9
version-control9
go6
dart6
Programming languages (7)
Github contributions (5)
Nearsoft/google-code-jam
Mar 2019 - May 2019
Code jam reference solutions
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:191 commits, 179 PRs, 104 pushes in 2 months
Contributions summary:Daniel primarily contributed to solutions for Google Code Jam problems, demonstrating proficiency in multiple programming languages including Scala and Java. Their work involved creating input/output mechanisms, implementing algorithms, and developing different approaches to solve algorithmic challenges. The user also worked on integrating and merging different branches of the code, showing experience with version control and code management.
jamcode-jam
minimaxir/textgenrnn
Jul 2018 - Jul 2018
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
Role in this project:ML Engineer
ML EngineerContributions:7 commits, 1 PR, 1 comment in 1 day
Contributions summary:Daniel primarily focused on enhancing the `textgenrnn` library's user interaction capabilities. They added an interactive mode to the text generation process, allowing users to choose from top-N predicted words or characters, undo choices, and stop generation. Furthermore, they implemented features to display text progress, correct slicing problems, and add manual word entries. These changes directly improve the user experience and control over the text generation process.
pytorchnlppythoncomplexitylines
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