Jake Neyer
Advisor at CargoLabs Inc.
Charlottesville, Virginia, United States
Summary
👤
Senior🎓
Top SchoolJake Neyer is an experienced software engineer and advisor with nine years building cloud-native and MLOps platforms, currently shaping solutions at Booz Allen and advising CargoLabs. He specializes in data movement and distributed systems—designing training schedulers, inference serving frameworks, event-driven microservices, and Kubernetes operators—while regularly coding in Go and Python. His background includes leading the Striveworks Chariot platform, open-sourcing components like Stampede and KubeGo Notebooks, and architecting hybrid cloud Kubernetes and OpenStack deployments. Prior roles in the Air Force and government contracting honed his skills in secure, large-scale infrastructure, IaC, and managing multi-account AWS/Kubernetes environments. Jake combines hands-on engineering with platform-level thinking to minimize deployment overhead and operational friction. Based in Charlottesville, VA, he brings a pragmatic focus on production-ready ML tooling and reliable data pipelines that move critical datasets at scale.
9 years of coding experience
10 years of employment as a software developer
Nanodegree Data Analytics, Nanodegree Data Analytics at UdacityAssociates in Information System Technology Information Technology, Associates in Information System Technology Information Technology at Community College of the Air ForceBachelor of Science - BS Data Science, Bachelor of Science - BS Data Science at Western Governors University
English
Github Skills (99)
terraform10
graph10
infrastructure-as-code10
monitoring9
lightgbm9
data-visualization9
dashboard9
grafana9
elasticsearch9
agentic9
mysql9
analytics9
seldon-core9
go9
prometheus9
Programming languages (10)
Github contributions (5)
polytomic/terraform-provider-polytomic
Jul 2022 - Mar 2023
Terraform provider for on premises Polytomic deploys.
Contributions:10 reviews, 62 commits, 230 PRs in 7 months
terraform-provider
Striveworks/LIteCOW
Mar 2021 - Apr 2022
Easily deploy inference models to dev, test, and production at scale
Contributions:6 PRs, 26 pushes, 2 branches in 1 year 1 month
inferenceknativeonnxmachine-learningkubernetes
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