Steven Robertson
Cloud Operations Engineer
Oakland, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSteven Robertson is a Cloud Operations Engineer at Palantir with 11 years of experience building and operating cloud-native and hybrid infrastructure. He maintains Mitogen for Ansible and contributes to the mitogen project, improving multi-interpreter Python support and handling complex ssh/sudo execution scenarios for a distributed Python orchestration library. At Intel AI and OctoML he combined backend Python development with DevOps, accelerating CI pipelines by over 5x, authoring core deployment code for MLT, and even engineering a bare‑metal k8s/device-plugin installer on CentOS6 without systemd. Based in Oakland and trained at UC Davis, he brings a rare mix of low-level systems troubleshooting, automation, and production-grade deployment experience.
11 years of coding experience
7 years of employment as a software developer
Bachelor of Science (BS), Computer Science, Bachelor of Science (BS), Computer Science at UC Davis
Spanish
Github Skills (26)
dockerce10
docker10
python10
bash10
dockers10
automation10
ansible10
ansible-galaxy10
automations10
devops10
sudo9
ci-cd9
ssh9
performancemonitor8
tensorflow28
Programming languages (6)
Github contributions (5)
mitogen-hq/mitogen
Oct 2019 - Mar 2021
Distributed self-replicating programs in Python
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:2 releases, 27 reviews, 301 commits in 1 year 5 months
Contributions summary:Steven primarily contributed to the `mitogen` project by enhancing its support for different Python interpreters, including special ones. They fixed issues related to the execution environment of the code. Moreover, the user made changes to support complex scenarios such as when the Ansible python interpreter requires specific path definitions. They modified aspects of the project relating to ssh and sudo commands.
raypythondevopsgeventinfrastructure
intel/ai-reference-models
Nov 2018 - Apr 2019
Intel® AI Reference Models: contains Intel optimizations for running deep learning workloads on Intel® Xeon® Scalable processors and Intel® Data Center GPUs
Role in this project:DevOps Engineer & Automation Engineer
DevOps Engineer & Automation EngineerContributions:12 commits, 8 PRs, 5 pushes in 4 months
Contributions summary:Steven's commits primarily focus on automating and improving the build and deployment processes within the repository. They made changes to the `start.sh` script to add functionalities and dependencies and updated the `launch_benchmark.py` script to enable enhanced debugging capabilities, including the ability to kill docker processes on Ctrl+C. The user also addressed Python 2/3 compatibility issues and implemented dynamic setting for the log directory. These contributions streamline the development and benchmarking workflows.
optimizationsprocessorstensorflowzoomodel-zoo
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