Waleed Abdulla
Principal Engineer Engineering Manager - CVML at Apple
Mountain View, California, United States
Summary
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RockstarWaleed Abdulla is a Principal Engineer and Engineering Manager leading a computer vision and machine learning organization at Apple with 12 years of experience delivering applied research into production. He blends hands-on ML engineering with people leadership, steering teams that translate cutting-edge CV/ML research into robust, production-ready systems from Mountain View. His open-source contributions include practical tooling for model visualization and training diagnostics (hiddenlayer) and performance- and compatibility-focused fixes to the widely used Mask R-CNN repository, underscoring his focus on reproducibility and developer ergonomics. Waleed is comfortable across the stack—model inspection, training pipeline optimization, and refactoring for evolving frameworks—so his teams ship reliable models despite shifting library APIs. Colleagues describe him as a pragmatic problem-solver who pairs technical depth with an ability to elevate code quality and operational readiness.
11 years of coding experience
Stackoverflow
1,913reputation
271kreached
13answers
7questions
console
top-5%
logging
top-5%
google-chrome
top-5%
Github Skills (25)
pytorch10
visualization10
python10
matplotlib10
machine-learning10
keras10
deep-learning10
instance-segmentation10
tensorflow10
neural-network10
object-detection10
visualizations10
console9
model-optimization9
google-chrome9
Programming languages (6)
Github contributions (5)
matterport/Mask_RCNN
Oct 2017 - Sep 2018
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:2 releases, 107 commits, 85 PRs in 11 months
Contributions summary:Waleed contributed to the codebase by implementing fixes related to TensorFlow versions and model inspection. Their work included modifying code to handle changes in non-maximum suppression (NMS) and adapting to deprecated libraries. Additionally, the user refactored the code, optimized training schedules, and updated comments, indicating a focus on code quality and model performance within the context of this machine learning project.
maskdeep-learningr-cnnobject-detectionmask-rcnn
waleedka/hiddenlayer
May 2018 - Feb 2019
Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras.
Role in this project:ML Engineer
ML EngineerContributions:1 release, 28 commits, 9 PRs in 9 months
Contributions summary:Waleed primarily contributed to the visualization and monitoring aspects of the `waleedka/hiddenlayer` repository, which focuses on neural network graphs and training metrics. Their commits modified the codebase to show tensors and images, fix training plots, and integrate weight histograms into the visualization tools. These changes included enhancements to the `Watcher` class and adjustments to the included demos, especially in the context of PyTorch and TensorFlow, highlighting the user's involvement in improving the presentation of machine learning model training.
pytorchdeep-learningmachine-learningtrainingneural-network
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