Cameron Craddock
Machine Learning Engineer at Meta
Austin, Texas, United States
Summary
👤
Senior🎓
Top SchoolCameron Craddock is a Machine Learning Engineer in Austin with 13 years of experience building and optimizing inference pipelines and computational neuroimaging systems. He blends academic rigor—PhD in ECE/Bioengineering from Georgia Tech and leadership roles running neuroimaging labs—with production ML engineering at companies including Meta and Indeed. As co-founder of The Neuro Bureau and a long-time contributor to the nipype project, he has improved workflow performance, added real-time memory monitoring, and fixed multiprocessing deadlocks to make large-scale neuroimaging pipelines more reliable. His background spans research scientist and director roles at major medical and research institutions, giving him deep domain expertise in brain imaging and experimental design. Known for translating complex scientific workflows into scalable, debuggable systems, he brings both hands-on performance engineering and strategic lab-to-production experience. An under-the-radar strength is his track record of instrumenting low-level runtime metrics that make hard-to-debug distributed jobs observable and maintainable.
13 years of coding experience
19 years of employment as a software developer
BCmpE, Computer Engineering, BCmpE, Computer Engineering at Georgia Institute of TechnologyHigh School, High School, High School, High School at Stanton College Preparatory School
Github Skills (13)
memory-profiling10
multi-process10
multiprocessing10
python-multiprocessing10
workflow-engine10
performance-optimization10
python10
neuroimaging10
dataflow9
debug9
dataflow-programming9
debugging9
big-data8
Programming languages (13)
Github contributions (5)
nipy/nipype
Nov 2015 - Dec 2016
Workflows and interfaces for neuroimaging packages
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:6 commits, 5 PRs, 15 comments in 1 year
Contributions summary:Cameron focused on improving the performance and functionality of the `nipype` neuroimaging workflow engine. Their primary contributions include adding real-time memory recording to the multiprocessing plugin and logging node runtime metrics. Further work involved fixing a deadlock issue within the multiprocessing plugin and implementing accurate logging of memory usage. The changes improved the monitoring capabilities and debugging of the workflow execution.
workflowneuroimagingpythondata-scienceworkflow-engine
ccraddock/ccraddock_cv
Mar 2015 - Jun 2021
Contributions:24 pushes in 6 years 3 months
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