Jason Carpenter
Staff MLOps Engineer at SandboxAQ
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolJason Carpenter is a Staff MLOps Engineer in Seattle with eight years of experience building production ML platforms and data pipelines for scientific and biomedical research. He has led multi-disciplinary teams to deliver self-serve compute environments, feature stores, and scalable orchestration (Airflow, Step Functions, Terraform) while maintaining security and compliance for enterprise users. At Manifold.AI he drove availability and platform robustness, partnered closely with power users, and shipped Text-to-SQL research tooling that accelerated analysts’ workflows. He contributes performance-focused open-source work—optimizing pandas operations and adding usability features to the swifter library—to speed real-world data processing. Now applying LLMs and quantum-aware tooling to drug discovery at SandboxAQ, he uniquely combines neuroscience research experience with hands-on MLOps engineering. Colleagues describe him as a pragmatic architect who turns research requirements into repeatable, production-grade systems.
8 years of coding experience
15 years of employment as a software developer
Machine Learning with R, Machine Learning with R at UCLA ExtensionMaster of Science Data Science, Master of Science Data Science at University of San FranciscoUniversity of California, Los Angeles
Github Skills (8)
pandas10
parallel-computing10
dask10
performance-optimization10
python10
text-processing9
strings9
numba7
Programming languages (5)
Github contributions (5)
jmcarpenter2/swifter
Aug 2018 - Aug 2022
A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
Role in this project:
Back-end Developer, Data Scientist
Back-end Developer, Data ScientistContributions:12 reviews, 347 commits, 129 PRs in 4 years
Contributions summary:Jason made contributions to the `swifter` library, which aims to speed up pandas operations. Their commits focused on optimizing string processing within the library, addressing performance bottlenecks and introducing functionality to handle these operations efficiently. They also worked on integrating features such as the inclusion of TQDM progress bar support and the ability to specify dask scheduler, further enhancing the library's utility and user experience.
data-analysispythonpolarsdataframesdata-science
jmcarpenter2/parfit
Sep 2018 - Apr 2020
A package for parallelizing the fit and flexibly scoring of sklearn machine learning models, with visualization routines.
Contributions:8 commits, 3 PRs, 95 pushes in 1 year 6 months
pythonscoringdata-sciencesklearnmachine-learning
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