Adam Reeve
Manager - Machine Learning Open Source Development at G-Research
Auckland, Auckland, New Zealand
Summary
🤩
Rockstar🎓
Top SchoolAdam Reeve is a manager and engineer with 16 years of experience building backend systems and open-source tooling for machine learning and data processing from Auckland, New Zealand. At G-Research he progressed from developer to Manager of Machine Learning Open Source Development, leading contributions that bridge research-grade ML workflows and production systems. His open-source work spans high-impact projects—MLflow (PyTorch autologging and step-based metrics), Apache Arrow (C# IPC compression and codec support), and Polars (non-equi join algorithms and Parquet improvements)—demonstrating deep cross-language systems expertise in Python, Rust, and .NET. He pairs a PhD in Biomedical Engineering with practical software engineering chops, an unusual blend that surfaces in careful numerical and performance-oriented contributions. Colleagues rely on him for pragmatic design, robust testing, and evolving developer ecosystems that make ML lifecycles more reliable.
16 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (PhD), Biomedical Engineering, Doctor of Philosophy (PhD), Biomedical Engineering at The University of Auckland
Github Skills (31)
pytorch10
python10
data-manipulation10
net10
dataframes10
query-optimization10
machine-learning10
dotnet10
mlflow10
pytorch-lightning10
ipc10
dataframe10
apache-arrow10
asp-net10
dotnet-core10
Programming languages (17)
Github contributions (5)
apache/arrow
Apr 2021 - Mar 2025
Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics
Role in this project:
Back-end Developer
Back-end DeveloperContributions:162 reviews, 45 PRs, 268 comments in 4 years
Contributions summary:Adam primarily contributed to the C# .NET implementation of the Apache Arrow project. They implemented IPC decompression support for the C# library and added a new package for compression codecs. They also added support for writing compressed IPC data and made numerous bug fixes for the C# implementation, including issues with slicing arrays, handling union arrays, decimal arrays, and writing to IPC format. The user also added integration tests to ensure the correct operation of the new functionality.
memorymulti-languagetoolboxacceleratedarrow
pola-rs/polars
Feb 2021 - Mar 2025
Dataframes powered by a multithreaded, vectorized query engine, written in Rust
Role in this project:Back-end Developer
Back-end DeveloperContributions:10 reviews, 10 PRs, 47 comments in 4 years 1 month
Contributions summary:Adam contributed to the Polars library, primarily focusing on improving data manipulation and query execution. Their work included enhancing horizontal concatenation features for LazyFrames and supporting byte stream split encoding in Parquet. Furthermore, the user implemented IEJoin algorithms for non-equi joins, and added benchmark tests for join_where with inequalities. They also improved documentation, clarifying behaviour.
polarsdataframespythondataframerust
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.