Runfei Luo
Manager Of Machine Learning at Pinterest
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolRunfei Luo is a machine learning leader with seven years of experience building and operationalizing ML at scale, currently managing ML teams at Pinterest after progressing from senior and staff engineering roles. With a Ph.D. in Statistics from UC Santa Barbara and a background in mathematics, Runfei blends rigorous statistical foundations with practical applied science honed at AWS as an Applied Scientist. He has hands-on expertise in reinforcement learning for production use cases—contributing RL examples and deployment-focused notebooks to the popular Amazon SageMaker examples repository—demonstrating a focus on real-world evaluation, checkpointing, and autoscaling. Known for bridging research and product, he mentors engineers while driving reproducible model pipelines and infrastructure.
7 years of coding experience
8 years of employment as a software developer
Master’s Degree Mathematics, Master’s Degree Mathematics at The University of ManchesterDoctor of Philosophy (Ph.D.) Statistics, Doctor of Philosophy (Ph.D.) Statistics at UC Santa Barbara
Github Skills (11)
amazon-sagemaker10
machine-learning10
jupyter-notebook10
python10
reinforcement-learning10
deep-learning9
mlops8
data-science8
aws8
trainings7
inference7
Programming languages (3)
Github contributions (5)
aws/amazon-sagemaker-examples
Nov 2018 - Oct 2020
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:
ML Engineer
ML EngineerContributions:31 reviews, 11 commits, 9 PRs in 1 year 10 months
Contributions summary:Runfei primarily contributed to reinforcement learning examples within the Amazon SageMaker environment. Their work involved developing and modifying Jupyter notebooks to showcase RL techniques for portfolio management, autoscaling, and other applications. They implemented and refined environments, integrated with the Coach and Ray RL frameworks, and addressed issues related to checkpointing and deployment, with several commits focused on evaluation steps. The user's contributions demonstrate a focus on practical applications of RL within the AWS ecosystem.
pythonjupyter-notebooktrainingawssagemaker
annaluo676/amazon-sagemaker-examples
Apr 2019 - Nov 2020
Example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker
Contributions:10 pushes, 5 branches in 1 year 7 months
sagemakeramazon-sagemakerdeep-learningamazonmachine-learning
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