Jason Kriss
Software Engineer, Machine Learning at Whatnot
Santa Monica, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJason Kriss is a machine learning leader and hands-on engineer with 13 years of experience building and scaling ML systems, teams, and platforms from startup co-founder to senior director roles. Based in Santa Monica, he has led cross-functional ML, data, and engineering organizations at companies like GOAT Group and Gametime and now focuses on ML engineering at Whatnot. He balances strategic team building and MLOps with deep technical contributions—evidenced by meaningful open-source work on PyTorch Ignite (supervised metrics, trainers) and practical front-end fixes to Ember.js. His background blends quantitative finance (MS in Mathematics in Finance), computer science training from Stanford, and entrepreneurial product experience, enabling him to translate research-grade models into production impact. Notably, he has repeatedly moved between individual contributor engineering and executive leadership, which gives him a rare fluency across modeling, platform, and operational concerns.
13 years of coding experience
13 years of employment as a software developer
Bachelor of Science, Mathematics and Economics, Bachelor of Science, Mathematics and Economics at University of MinnesotaMaster of Science, Mathematics in Finance, Master of Science, Mathematics in Finance at New York UniversityGraduate Certificate, Computer Science, Graduate Certificate, Computer Science at Stanford University
Stackoverflow
1reputation
0reached
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Github Skills (16)
pytorch10
machine-learning10
javascript10
handlebars10
python10
ember10
metric10
deeplearning-ai9
deep-learning9
url-routing9
javascript-framework9
unit-testing8
neural-network8
unit-test8
pytest8
Programming languages (11)
Github contributions (5)
pytorch/ignite
Jan 2018 - May 2018
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
Role in this project:
ML Engineer
ML EngineerContributions:21 commits, 44 PRs, 18 pushes in 4 months
Contributions summary:Jason significantly contributed to the development of metrics for PyTorch Ignite, introducing supervised metrics such as accuracy, mean squared error, and recall. They implemented the core logic, added tests, and integrated the metrics with the existing framework. Further contributions include a factory function for creating supervised trainers, and adding of an output transform functionality to the metrics.
neural-networkpytorchpythonmachine-learningdeep-learning
emberjs/ember.js
Mar 2013 - Apr 2013
Ember.js - A JavaScript framework for creating ambitious web applications
Role in this project:Front-end Developer
Front-end DeveloperContributions:8 commits in 1 month
Contributions summary:Jason primarily focused on improving the Ember.js framework's codebase by fixing typos, removing unused variables, and clarifying documentation. Their commits demonstrate a strong understanding of the framework's internal workings, including routing, templating, and view components. They also contributed to code style improvements and ensured adherence to best practices, impacting the overall quality and maintainability of the framework.
emberjsjavascript-frameworkemberjavascript
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