Ning Rogers
Staff Software Engineer at Meta
Redmond, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolNing Rogers is a Staff Software Engineer with 11 years of experience building large-scale, privacy-conscious ad and cloud systems from Microsoft to Google and now Meta in Redmond. He has driven backend engineering for ads monetization and attribution, led multi-cloud BigQuery migration work at Google, and contributed to ML tooling improvements in high-profile open-source projects like PyTorch Lightning, focusing on robust data handling for distributed training. Known for navigating complex privacy and signal-loss challenges in ad infrastructure, he combines systems-level thinking with hands-on changes that improve data pipelines and model training workflows. Ning holds an MS in Computer Science from NYU and a foundation in electronic engineering, bringing a rare mix of low-level systems, cloud orchestration, and ML engineering expertise.
11 years of coding experience
13 years of employment as a software developer
Master of Science, Computer Science, Master of Science, Computer Science at New York UniversityElectronic Engineering, Telecommunication, Electronic Engineering, Telecommunication at Huazhong University of Science and Technology
Github Skills (6)
pytorch10
machine-learning10
pytorch-lightning10
deep-learning10
python10
data-science10
Programming languages (3)
Github contributions (5)
Lightning-AI/pytorch-lightning
Aug 2021 - Mar 2022
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
Role in this project:
ML Engineer
ML EngineerContributions:48 reviews, 7 commits, 15 PRs in 7 months
Contributions summary:Ning's contributions primarily focus on deprecating and modifying functionalities related to data preparation within the PyTorch Lightning framework. They updated code related to `prepare_data_per_node`, aligning its settings with best practices, and deprecated outdated hooks. The commits also include changes to dataloader management and batch size scaling within the context of a machine learning training workflow. These modifications indicate a focus on improving the data handling aspects of the model training process.
pythonheadachespytorch-modelsdata-sciencehandling
ninginthecloud/pytorch-lightning
Jul 2021 - May 2022
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
Contributions:117 pushes, 16 branches in 9 months
pytorchpythonboilerplatedeep-learningmachine-learning
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