Pavel Simakov
United States
Summary
🤩
Rockstar🎓
Top SchoolPavel Simakov is a hands-on technology leader and Managing Partner with 13+ years building large-scale distributed systems, developer platforms, and secure software supply chains across Google, PayPal, Bridgewater, and startups. He helps organizations adopt practical, human-centered AI and cloud architectures, blending strategic advising with deep implementation experience in CI/CD, SLSA, SBOM, and LLM-driven workflows. At Google Cloud he led developer-focused AI & BI initiatives and experiments in agentic architectures, and his engineering work includes back-end and DevOps contributions to notable projects like Google’s eng-edu Datalab tooling. Pavel’s background in domain-specific languages and macroeconomic modeling at Bridgewater, combined with a PhD in physical organic chemistry, gives him a rare mix of rigorous scientific thinking and production-grade software craftsmanship. He serves on the Human Feedback Foundation board, advocating for democratic, open RLHF and global human-feedback infrastructure for safer AI. Colleagues rely on him to turn complex, security-sensitive AI ambitions into scalable, auditable systems that augment—rather than replace—expert humans.
13 years of coding experience
5 years of employment as a software developer
Ph.D., Physical Organic Chemistry, Ph.D., Physical Organic Chemistry at Wayne State UniversityLomonosov Moscow State University
English, Russian
Github Skills (15)
google-cloud-sdk10
python10
gcp10
devops9
automation9
automations9
bash9
docker8
mle8
ml8
dockers8
tensorflow8
system-design7
apidoc7
api7
Programming languages (5)
Github contributions (5)
google/eng-edu
Feb 2017 - Jun 2017
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:65 commits, 1 PR, 65 pushes in 3 months
Contributions summary:Pavel primarily worked on the back-end infrastructure for creating and managing Datalab VM projects. They implemented scripts to enable Google Compute Engine and Cloud Machine Learning APIs, provision new Datalab VMs, and manage project creation and deletion in bulk. Moreover, the user refactored provisioning functions in Python, enhancing code quality with retries, concurrent execution, and tests, and also added support to deploy content bundles. They also updated tooling components and corrected scripts.
google/pyaedj
Nov 2017 - Nov 2021
Contributions:14 commits, 13 pushes, 1 branch in 4 years
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