Suresh Alse is a senior Machine Learning Engineer with 14 years of experience, currently leading advanced ML work at Adobe Sensei in San Francisco. His career at Adobe spans from building core computer-vision components—face alignment, recognition, object detection and masking—to architecting production ML systems as an MLE5. He blends research-driven approaches from his USC/ISI work on semantic labeling and large-scale data integration with pragmatic engineering demonstrated at Intuit and in open-source projects. On GitHub he has full‑stack experience (e.g., crontab-ui) and backend contributions integrating semantic typing into data pipelines, showing a rare mix of frontend, backend and ML expertise. Colleagues know him for turning algorithmic ideas into maintainable, production-ready features across consumer and enterprise products.
14 years of coding experience
8 years of employment as a software developer
Master’s Degree, Computer Science, Master’s Degree, Computer Science at University of Southern California
Bachelor of Technology (BTech), Information Technology, Bachelor of Technology (BTech), Information Technology at National Institute of Technology Karnataka
Contributions:16 releases, 3 reviews, 168 commits in 7 years 8 months
Contributions summary:Suresh primarily contributed to the development of the crontab-ui project. Their work included setting up the basic project structure and integrating Bootstrap for the UI. The user implemented features for reading crontab entries from a database and displaying them within the UI. They also added functionalities for creating, editing, stopping, starting and deleting crontab jobs, and added a basic backup feature.
Contributions:52 commits, 10 PRs, 4 comments in 2 months
Contributions summary:Suresh focused on adding semantic types to the server during the R2RML model publishing process. Their contributions involved modifying Java code within the `karma-common` module to integrate with a semantic labeling service. This likely involved connecting to an external service for automated type assignment based on the R2RML models. The user also updated configuration files to enable online semantic typing features and incorporated the upload of user-defined semantic types.
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Suresh Alse - Machine Learning Engineer 5 at Adobe