Dan Rammer
Senior Software Engineer at LanceDB
Madison, Wisconsin, United States
Summary
🤩
Rockstar🎓
Top SchoolDan Rammer is a Senior Software Engineer and PhD-trained distributed systems specialist with a decade of experience building high-performance backend components for data analytics, storage, and workflow orchestration. He has driven major scalability and performance wins at Union.ai—leading Flyte execution services, designing an Actors project that sped task execution up to 5x, and authoring FlytePropeller improvements now central to the open-source project. His research at Colorado State produced systems that cut I/O and training time by orders of magnitude, work he has translated into production-grade cloud-native solutions. Now contributing to LanceDB and continuing OSS work on Flyte, he blends deep systems thinking with hands-on DevOps and benchmarking to solve real-world scale problems. Based in Madison, WI, he balances rigorous engineering with an off-grid life of running, biking, hunting, and camping—a reminder that his focus on efficiency extends beyond code.
11 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science (Distributed Systems), Doctor of Philosophy - PhD Computer Science (Distributed Systems) at Colorado State UniversityBachelor of Science Computer Science, Bachelor of Science Computer Science at University of Wisconsin–Oshkosh
Github Skills (20)
kubernetes10
orchestrate10
helm10
kubernetes-pods10
orchestrator-workflow10
orchestration10
engine10
devops10
performance-analytics9
performance-monitor9
performance-measurement9
system-design9
performance-monitoring9
golang9
go9
Programming languages (9)
Github contributions (5)
flyteorg/flyte
Sep 2021 - Jan 2023
Dynamic, resilient AI orchestration. Coordinate data, models, and compute as you build AI workflows.
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:208 reviews, 51 commits, 156 PRs in 1 year 3 months
Contributions summary:Dan primarily contributed to the documentation and configuration related to FlytePropeller, a core component of the workflow orchestration platform. Their work included adding architecture documents, updating helm charts for sharding, and integrating container-level configurations. Additionally, the user addressed performance concerns by adding warnings related to the Kube API server throttling and providing guidance on improving etcd performance. These contributions demonstrate their focus on system architecture, deployment, and performance optimization.
aiai-workflowsflytemachine-learninggolang
hamersaw/dotfiles
Jun 2018 - Feb 2026
Contributions:46 pushes, 1 branch in 7 years 9 months
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