Miguel Toledo
Co-Founder & CTO at Flex
Atlanta Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolMiguel Toledo is a Co-Founder and CTO with a decade of experience building production ML infrastructure and backend systems across startups and large tech organizations. He has driven engineering at companies from Lyft’s Level 5 self-driving division to Unit21’s new business team, and now leads technology at Flex in the Atlanta area. Hands-on in both infra and product, he contributed notable enhancements to the popular Flyte workflow SDK—adding interruptible tasks, queuing budgets, and container resource controls—demonstrating deep familiarity with distributed workflow execution. Miguel’s background includes roles at Woven Planet and internships at Google and Equifax, reflecting a steady progression from systems intern to technical founder. He combines practical engineering chops with startup instincts, frequently shipping cross-cutting changes that improve reliability and resource management. Based in the U.S., he pairs a Kennesaw State Computer Science degree with entrepreneurial training from Y Combinator.
10 years of coding experience
5 years of employment as a software developer
Y CombinatorBachelor’s Degree Computer Science, Bachelor’s Degree Computer Science at Kennesaw State University
English, Spanish
Stackoverflow
1reputation
0reached
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0questions
Github Skills (12)
argo-workflows10
flyte10
workflow-engine10
workflow-automation10
sdk10
python10
automation9
automations9
spark8
data-science8
kubernetes6
kubernetes-pods6
Programming languages (5)
Github contributions (5)
flyteorg/flytekit
Mar 2020 - Jul 2021
Extensible Python SDK for developing Flyte tasks and workflows. Simple to get started and learn and highly extensible.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:4 releases, 2 reviews, 43 commits in 1 year 4 months
Contributions summary:Miguel focused on enhancing the Flytekit Python SDK, particularly concerning task and workflow capabilities. They added an "interruptible" parameter to tasks and workflows, modifying existing code across multiple task types (Python, Spark, Hive, etc.). The user also implemented the ability to specify queuing budgets in workflow metadata. Furthermore, they addressed issues related to Spark task execution and serialization processes. Finally, the user added request and limits parameter to ContainerTask.
flytepythondataflyte-taskspypi
csheare/Sendator
Sep 2017 - Sep 2017
Contributions:21 pushes in 1 day
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