Chase Roberts
Senior Software Engineer - Distributed JAX at NVIDIA
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolChase Roberts is a Senior Software Engineer with 11 years of experience specializing in distributed ML systems and compiler/back-end work, currently focused on Distributed JAX at NVIDIA. He brings deep expertise in GPU/XLA backends and parallel compute primitives, having contributed Collective Broadcast support to TensorFlow’s XLA/GPU stack and core parallel features to JAX. His background spans research and applied roles across Google, X (Moonshot Factory), Xanadu, and Recogni, bridging quantum software, accelerator firmware, and machine learning research. An RPI-trained mathematician and computer scientist, Chase pairs rigorous theory with production-grade engineering—his work surfaces in high-profile open-source projects like TensorFlow and JAX. A concise hint of his style: his GitHub bio “c += a @ b” reflects a taste for elegant, composable abstractions in numerical computing.
11 years of coding experience
4 years of employment as a software developer
Bachelor's degree, Mathematics and Computer Science, Bachelor's degree, Mathematics and Computer Science at Rensselaer Polytechnic Institute
English, Hindi
Github Skills (16)
mle10
xla10
machine-learning10
c-language10
tensorflow10
parallel-computing10
cprogramming-language10
gpu10
jax10
python10
back-end-development10
ml10
deep-learning9
gpu-programming9
mlops8
Programming languages (15)
Github contributions (5)
jax-ml/jax
May 2019 - Feb 2025
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Role in this project:
ML Engineer
ML EngineerContributions:22 reviews, 20 PRs, 90 comments in 5 years 9 months
Contributions summary:Chase primarily contributed to the JAX library, focusing on features related to parallel processing and compute type annotations. They implemented the `pbroadcast` function, integrated stream annotation support for GPU computation, and added necessary imports for core JAX functionalities. Additionally, the user made changes to the testing framework to validate these new features. Their work directly supports the core functionalities of JAX, namely, composable transformations of Python+NumPy programs, specifically demonstrating skills in parallel computing.
pytorchpythonjitautomatic-differentiationgpu
tensorflow/tensorflow
Nov 2015 - Feb 2020
An Open Source Machine Learning Framework for Everyone
Role in this project:Back-end Developer
Back-end DeveloperContributions:1 PR, 9 comments, 1 issue in 4 years 3 months
Contributions summary:Chase primarily contributed to the XLA/GPU backend, specifically adding support for Collective Broadcast operations. They introduced new HLO instructions, updated related header and source files, and incorporated Collective Broadcast support in the XLA builder API, LMHLO dialect, and AsyncCollectiveCreator. Furthermore, the user added code for the latency hiding scheduler and XLA/GPU backend. These changes enhanced the XLA framework's ability to handle collective communication operations.
pythondata-sciencedeep-learningmlmachine-learning
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