Joshua Tree Karpel

Also available as PDF or Markdown.

Skills

  • Product-minded technical leadership of engineering teams
  • Platform, service, and developer tooling engineering, from laptop to production
  • Software architecture, from libraries to distributed systems
  • Understanding existing code, then refactoring/extracting/migrating/replacing it
  • Explaining systems to executives, product managers, and engineers alike
  • AI/ML infrastructure, with observability and CI/CD
  • Performance and cost optimization, without trading one for the other
  • Python primarily, plus Rust and TypeScript in personal projects

Work Experience

Workday

November 2020 – Present

Principal Machine Learning Engineer

November 2023 – Present
  • Served as tech lead of our Ray-based model-serving system, which we grew into the company-wide platform for model hosting and external LLM access; designed the sharding system that uses rendezvous hashing to spread tens of thousands of models across several physical clusters behind one logical service.
  • Reduced our organization's cloud spend by 40% while improving latency and reliability across services within and on top of the AI platform, by optimizing how they use infrastructure rather than just cutting the capacity they run on.
  • Helped design and build an Argo CD-based deployment system for the AI platform and the ~150 services on our Kubernetes clusters, cutting pull-request-to-production time from eight hours to three minutes, with Rego policy checks on rendered Helm and Kustomize manifests, so bad configuration fails in CI.

Senior Machine Learning Engineer

November 2021 – November 2023
  • Led the original design and build of the model-serving system, 50x cheaper than the one it replaced; won a Workday Innovator Award for it in 2023.
  • Helped design and build the monorepo that brought our shared inference service framework to dozens of services and hundreds of engineers.
  • Migrated a service's model-building and ETL pipelines from our colocated data center to AWS, trading a fixed hardware footprint for elastic capacity; won one of 25 quarterly Outstanding Contributor Awards for the work in Fall 2022.

Machine Learning Engineer

November 2020 – November 2021
  • Factored a shareable inference service framework out of an existing service, giving later services a common base to build on.
  • Built tooling for defining our alerts and dashboards programmatically, so that observability was reviewed and versioned like the rest of our code.

Center for High-Throughput Computing

Summer 2018 – November 2020

Morgridge Postdoctoral Fellow

January 2020 – November 2020
Core Computational Technology Group at the Morgridge Institute for Research
  • Worked with researchers to turn their science into automated high-throughput workflows, generalizing one-off solutions into shared tools like Dask-CHTC.
  • Modernized how HTCondor taught and tested itself: rewrote the Python bindings tutorials for users rather than administrators, and built Ornithology, a pytest-based alternative to the existing Perl-based testing framework.

Project Assistant

Summer 2018 – Fall 2019
  • Primary developer of HTMap and htcondor.dags (now part of HTCondor itself), Python packages for describing and running HTCondor workflows.

UW-Madison Physics Department

Fall 2014 – Fall 2019

Graduate Research Assistant

Summer 2015 – Fall 2019

Graduate Teaching Assistant

Fall 2014 – Spring 2018

Selected Talks

all talks

Education

Ph.D. in Physics

Fall 2014 – Fall 2019
University of Wisconsin-Madison

B.A. in Physics and Mathematics

Fall 2010 – Spring 2014
University of Colorado-Boulder