Jesse Nicholson
Senior Software Engineer at Storable
Cary, North Carolina, United States
Summary
👤
Senior🎓
Top SchoolJesse Nicholson is a Senior Software Engineer with 11 years of experience building reliable, fault-tolerant systems across C#, Java, and Erlang, now working at Storable in Cary, NC. He has a strong background in full lifecycle delivery—design, testing, deployment, and support—and has repeatedly operated in roles emphasizing uptime and resilience. Jesse contributes to open-source ML tooling, notably adding modern TensorFlow 2/TFHub training, EfficientNet integration, hyperparameter tuning, and model quantization to a popular NSFW detection repo, showing practical ML engineering chops beyond typical backend work. Comfortable leading small teams and Scrum processes, he mixes hands-on coding with client-focused product delivery and has roots in volunteer and competitive-event software projects.
11 years of coding experience
14 years of employment as a software developer
Bachelor of science (BS), Information Technology - Programming, Bachelor of science (BS), Information Technology - Programming at University of Arkansas - Fort Smith
Stackoverflow
53reputation
11kreached
3answers
3questions
Github Skills (16)
keras10
machine-learning10
tensorflow10
trainings10
python10
modeling10
computer-vision9
efficientnet9
sql-server6
apache-spark6
google-cloud-platform6
windows-authentication6
jupyterlab6
fixed-width6
jdbc6
Programming languages (20)
Github contributions (5)
GantMan/nsfw_model
Feb 2020 - May 2020
Keras model of NSFW detector
Role in this project:
ML Engineer
ML EngineerContributions:12 commits, 6 PRs, 54 comments in 2 months
Contributions summary:Jesse significantly contributed to the development of the NSFW detection model. They added TF2 and TFHub-based training capabilities, demonstrating an understanding of modern TensorFlow practices. The user also modified training scripts to optimize hyperparameters, including setting up early stopping and various optimizer configurations, and added the functionality to quantize the web model. The user integrated EfficientNet B0 model and the necessary parameters to make the training process flexible with a variety of optimizers and configurations.
inceptionv3keras-modelmachinelearningdeep-learningmachine-learning
TechnikEmpire/NsfwNET
Dec 2017 - Apr 2021
Contributions:4 releases, 18 commits, 17 pushes in 3 years 4 months
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