William Gleich
Member Of The Technical Staff at Databricks
Salt Lake City, Utah, United States
Summary
🤩
Rockstar🎓
Top SchoolWilliam Gleich is a Member of the Technical Staff with nine years of hands-on experience building and operating cloud-native infrastructure and MLOps platforms. He has progressed from systems administration and automation at the University of Utah to senior DevOps and MLOps roles at Proofpoint, MosaicML, and now Databricks, specializing in AWS, Kubernetes/EKS, GitOps, Terraform, and CI/CD. William has deep practical experience optimizing build and training environments for large ML frameworks—his contributions to MosaicML’s Composer repo included upgrading OFI/NCCL and Mofed drivers to better integrate with AWS for scalable model training. He pairs infrastructure automation with developer-focused tooling, having built Python and Flask automation for certificate management, Active Directory provisioning, and Kubernetes deployment pipelines. Comfortable in both research and enterprise contexts, he brings a background in chemistry and business that helps him translate technical constraints into operational solutions. Colleagues describe him as a pragmatic problem-solver who prefers improving platform reliability and developer velocity through thoughtful automation.
9 years of coding experience
7 years of employment as a software developer
The University of Utah
Stackoverflow
368reputation
18kreached
8answers
2questions
Github Skills (20)
docker10
dockers10
aws10
pytorch9
machine-learning9
python8
mlops8
artificial-neural-networks7
neural-network7
cicd7
deeplearning-ai7
deep-learning7
amazon-sagemaker6
amazon-s36
amazon-web-services6
Programming languages (13)
Github contributions (5)
mosaicml/composer
Mar 2023 - Oct 2024
Supercharge Your Model Training
Role in this project:
DevOps Engineer
DevOps EngineerContributions:8 PRs, 12 pushes, 9 branches in 1 year 7 months
Contributions summary:William focused on configuring and updating the build environment for the `composer` repository, a machine learning training framework. Their contributions involved modifying the `docker/generate_build_matrix.py` file to bump the AWS OFI NCCL version, upgrade the Mofed drivers, and integrate with the AWS platform. These changes likely impact the build process and environment configuration for training deep learning models.
pytorchml-systemsdeep-learningneural-networksmachine-learning
willgleich/gleich.tech
May 2019 - Nov 2024
Contributions:2 PRs, 89 pushes, 4 branches in 5 years 6 months
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