Jordan Edwards
Product Manager at Meta
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolJordan Edwards is a product leader with 14 years in product management and over a decade designing ML training and model management platforms for large-scale AI systems. Currently at Meta, he leads the Model Training Platform powering GenAI and recommendation workloads; previously he was Principal Group PM for Azure Machine Learning at Microsoft, shaping model catalogs, experiment tracking, and deployment integrations. He blends deep technical fluency—hands-on contributions to open-source Azure ML MLOps pipelines and IaC for AKS—with strong product strategy and team leadership. Jordan’s background spans developer platforms, Bing’s data and metrics infrastructure, and early work in data analysis and web development, giving him cross-domain credibility from infrastructure to ML research tooling. Based in Seattle, he’s as comfortable defining roadmaps and collaboration features as he is reviewing model explainability pipelines and MLflow-integrated training workflows.
10 years of coding experience
12 years of employment as a software developer
BS Computer Science, BS Computer Science at Cornell UniversityInternational Baccalaureate Program, International Baccalaureate Program at Hilton Head Island High School
Stackoverflow
11reputation
130reached
1answer
0questions
Github Skills (21)
python10
machine-learning-models10
scikit10
machine-learning10
mlflow10
microsoft-azure10
azure-machine-learning10
ml-deployment10
azure-cli10
mlops10
aac10
scikit-learn10
infrastructure-as-code10
azure10
continuous-deployment9
Programming languages (8)
Github contributions (5)
microsoft/MLOps
May 2019 - Jun 2021
MLOps examples
Role in this project:
MLOps Engineer
MLOps EngineerContributions:185 commits, 36 PRs, 180 pushes in 2 years 1 month
Contributions summary:Jordan created scripts to set up and configure Azure Machine Learning resources, specifically focusing on infrastructure-as-code (IaC) for AKS compute targets, workspaces, and compute instances. They demonstrated experience with Azure CLI commands to attach datastores and deploy machine learning models. Additionally, the user worked on setting up an explainability pipeline within Azure ML and included sample code for deploying and scoring a model with explanations.
cmlmlopsmachine-learningazure-machine-learningkubernetes
MicrosoftDocs/pipelines-azureml
Apr 2019 - Aug 2020
Example Azure Pipeline to train and deploy a machine learning model using the Azure Machine Learning service
Role in this project:ML Engineer
ML EngineerContributions:83 commits, 2 PRs, 73 pushes in 1 year 4 months
Contributions summary:Jordan primarily contributed to the development and modification of machine learning model training and deployment pipelines within the Azure Machine Learning service. They implemented model training scripts using scikit-learn, defined model input/output schemas, and integrated MLflow for experiment tracking. Furthermore, the user updated model deployment code and integrated testing for the environment setup. The commits demonstrate expertise in machine learning model development and deployment on Azure.
pipelinemachine-learningazure-machine-learningmlopsazure
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