Yuri Chebotarev
Member Of Technical Staff at OpenAI
Bellevue, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolYuri Chebotarev is a senior applied machine learning and software engineer with 11 years in ML and ~15 years overall in software development, now at OpenAI after recent roles at Temporal, Amazon, and Microsoft. He blends deep statistical training (Master’s in Statistics from Penn State) with extensive systems and backend experience, shipping robust distributed solutions for storage, telemetry, and workflow orchestration. His work spans from low-level performance optimizations and high-availability telemetry systems to production ML that turns storage data into actionable improvements. An active contributor to Temporal, he has improved workflow reliability, error masking, and search attribute filtering—demonstrating fluency in both distributed services and ML-driven product features. Curious and experimental by nature, he pursues both theoretical and practical advances in deep learning and big-data processing. Based in Bellevue, WA, he pairs a research-oriented mindset with a long track record of delivering pragmatic, scalable engineering results.
11 years of coding experience
28 years of employment as a software developer
Master's degree, Statistics, 3.8, Master's degree, Statistics, 3.8 at Penn State UniversityMaster's degree, Computer Science, Master's degree, Computer Science at Odessa Polytechnic National University
Github Skills (9)
error-handling10
distributed-systems10
go10
grpc10
testing9
microservices-application9
workflow-management9
microservices9
workflow-engine8
Programming languages (4)
Github contributions (5)
temporalio/temporal
May 2024 - Apr 2025
Temporal service
Role in this project:
Back-end Developer
Back-end DeveloperContributions:515 reviews, 187 PRs, 467 pushes in 10 months
Contributions summary:Yuri's contributions focus on enhancing the Temporal service by addressing internal error handling, optimizing workflow execution, and improving various aspects of the system. Their work includes adding an interceptor to mask internal errors, implementing a grace period for workflow restarts to prevent conflicts, and integrating search attribute capabilities for filtering workflows. The user also contributed to improving the reliability of activity execution and modifying the batch query functionality.
service-busmicroservice-orchestrationworkflow-automationworkflow-enginedocker
ychebotarev/temporal
May 2024 - Jan 2025
Temporal service
Contributions:99 pushes, 5 branches in 8 months
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