Charles Parker
Director Of Platform Development
Lake Bluff, Illinois, United States
Summary
👤
Senior🎓
Top SchoolCharles Parker is a Director of Platform Development and Ph.D.-trained machine learning researcher with 14 years of experience building production ML systems, data pipelines, and high-performance platforms. He has led cross-functional teams to consolidate legacy stacks into standardized Python/Kubernetes infrastructures, delivering 3x–100x performance gains and enabling widespread (80%) adoption of AI-assisted development practices. His background spans applied research in computer vision and data mining to commercial ML SaaS leadership—driving algorithmic product expansion, Bayesian optimization, and model deployment at scale. As a founder and principal scientist he has practical experience with LLM/NLP integration for health-focused products and has contributed open-source ML work such as a predictive LDA implementation in BigML’s Python bindings. He combines deep academic rigor with a pragmatic focus on operationalizing models across cloud and on-prem environments, and frequently translates research prototypes into hardened production services. Based in Lake Bluff, Illinois, he is comfortable at the intersection of product, engineering, and go-to-market technical advocacy.
14 years of coding experience
23 years of employment as a software developer
Ph. D. Computer Science, Ph. D. Computer Science at Oregon State UniversityB.Sc. Electrical and Computer Engineering, B.Sc. Electrical and Computer Engineering at Northwestern University
Github Skills (9)
model-building10
machine-learning10
python10
modeling10
model-driven10
model-driven-development10
api9
stemming8
numpy7
Programming languages (6)
Github contributions (5)
bigmlcom/python
Aug 2016 - Dec 2021
Python bindings for BigML.io
Role in this project:
ML Engineer
ML EngineerContributions:41 commits, 7 PRs, 9 pushes in 5 years 4 months
Contributions summary:Charles primarily contributed to the development of a local predictive LDA (Latent Dirichlet Allocation) model for the BigML.io Python bindings. Their work involved adding the LDA model, refactoring variable names, fixing misnamed fields, and improving code comments. The user implemented a `distribution` function to infer topic distributions from input text, demonstrating a focus on machine learning model integration.
apipythonpython-bindingsbigmlmachine-learning
charleslparker/examples
Sep 2016 - Nov 2019
Contributions:1 PR, 53 pushes, 3 branches in 3 years 2 months
machine-learning
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