Chris Olivier
Staff Engineer at Tesla
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolChris Olivier is a Staff Engineer based in Palo Alto with a decade of experience building high-performance back-end systems and ML infrastructure across companies from AWS and Cerebras to Confluent and Tesla. He combines deep C++/CUDA and build-system expertise with distributed systems knowledge, contributing to flagship open-source projects like Apache MXNet and DMLC to improve sparse ops, GPU builds, and cross-platform stability. At Confluent he strengthened Kafka Connect cloud and Elasticsearch connectors, focusing on robustness, testing, and real-world interoperability, and at Tesla he continues to drive large-scale engineering solutions. A long-time Apache PMC committer, he brings an operator’s attention to build reliability and platform compatibility—often surfacing in seemingly small but critical fixes such as compiler warnings, CMake adjustments, and CI/test improvements. Colleagues would note his pragmatic blend of performance optimization and devops discipline, and his GitHub bio deceptively understates a history of impactful, low-level contributions.
10 years of coding experience
12 years of employment as a software developer
University of Florida
Stackoverflow
124reputation
18kreached
5answers
0questions
Github Skills (58)
kafka-connect10
c-language10
back-end-development10
python10
multithreading10
amazon-elasticsearch10
testing10
sparse-matrix10
kafka10
machine-learning10
build-system10
cmake10
java10
javas10
elasticsearchquery10
Programming languages (12)
Github contributions (5)
apache/mxnet
Aug 2016 - Dec 2019
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:11 releases, 1 review, 89 commits in 3 years 3 months
Contributions summary:Chris contributed to the Caffe data layer converter, adding the ability to convert Caffe data layers to MXIter format. They also worked on fixing various build errors and linting issues, indicating a focus on improving the overall project quality. The contributions include implementing and optimizing the performance of sparse operations in the library. They have also implemented features related to the performance of dropout operators.
pythonschedulerdataflowmutationdata-science
dmlc/dmlc-core
Aug 2016 - Mar 2018
A common bricks library for building scalable and portable distributed machine learning.
Role in this project:Back-end Developer
Back-end DeveloperContributions:14 commits, 18 PRs, 52 comments in 1 year 6 months
Contributions summary:Chris primarily focused on fixing warnings, adjusting CMake configurations, and addressing compilation errors within the `dmlc-core` repository. They also made changes related to environment variables and made modifications to address compiler warnings in code files. Additionally, the user added external lockfree queue classes, updated thread lifecycle management, and made several compile-time fixes to support Windows builds. These commits suggest a focus on code quality, build stability, and platform compatibility.
scalablebricksmachine-learningportabledistributed-machine-learning
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