Danylo Baibak
Senior Machine Learning Engineer Team Lead at eBay
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolDanylo Baibak is a Senior Machine Learning Engineer and team lead based in Berlin with 11 years of experience designing and shipping ML systems from research to production at scale. He has driven ML and AI infrastructure work at Meta and now leads applied ML efforts at eBay, combining hands-on engineering with team leadership. His background spans recommendation systems, NLP, CI/CD automation and full-stack development across fintech and marketplace domains, with earlier roots in engineering physics. An active open-source contributor, he improved PyTorch CI/CD workflows to support diverse runner types and automated Docker image rollouts, showing a strong focus on reliable, multi-platform ML delivery. Always learning, he excels at turning complex models into maintainable, production-ready services that solve real business problems.
11 years of coding experience
13 years of employment as a software developer
National Technical University "Kharkiv Polytechnic Institute"
English, Ukrainian, Russian
Stackoverflow
2,236reputation
197kreached
106answers
0questions
python
top-5%
machine-learning
top-5%
scikit-learn
top-5%
keras
top-5%
Github Skills (22)
github-ci10
docker10
dockers10
cicd10
automation10
automations10
githubaction-workflow10
python9
machine-learning9
scikit-learn9
keras9
infrastructure7
cloud-infrastructure7
neural-network6
regression6
Programming languages (5)
Github contributions (5)
pytorch/pytorch
Jul 2022 - Jul 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
DevOps Engineer & Automation Engineer
DevOps Engineer & Automation EngineerContributions:76 reviews, 5 commits, 78 PRs in 1 day
Contributions summary:Danylo primarily contributed to the repository by implementing and modifying CI/CD workflows. They added logic for pushing Docker images to the ghcr.io registry, including necessary authentication. Furthermore, the user modified the GitHub Actions workflows to utilize different runner types, including `linux.arm64.2xlarge` and `macos-m1-stable`, demonstrating an understanding of build environment configuration. They also introduced a dynamic rollout mechanism for switching between ARC and old runners, adding flexibility to the testing infrastructure.
pythongpu-accelerationdeep-learninggpunumpy
DanilBaibak/scikit-learn
Dec 2017 - Apr 2019
scikit-learn: machine learning in Python
Contributions:38 pushes, 2 branches in 1 year 4 months
pythondata-sciencelearn-machine-learningmachine-learningscikit-learn
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