Mohammed Yasin
Senior Machine Learning Engineer at Ultralytics
Selangor, Malaysia
Summary
🤩
Rockstar🎓
Top SchoolMohammed Yasin is a Senior Machine Learning Engineer based in Selangor, Malaysia, with eight years of experience specializing in computer vision and production-ready ML systems. He has deep practical expertise optimizing inference pipelines and multi-GPU deployments—work that enabled real-time analytics across hundreds of streams and solved critical stability and throughput bottlenecks. At Ultralytics he progressed from community moderator to senior engineer, contributing 147+ PRs and key fixes to the widely used Ultralytics YOLO repository, improving training, export (ONNX, TensorRT, TFLite, CoreML) and validation for detection, segmentation and keypoint models. Comfortable working without GPUs, he builds efficient architectures and batching strategies that squeeze high FPS from constrained hardware. He pairs hands-on engineering with community-driven troubleshooting, having answered thousands of issues across GitHub and forums to improve user experience. Currently pursuing MPhil research in Computer Science, he brings both academic curiosity and production discipline to challenging CV problems.
8 years of coding experience
2 years of employment as a software developer
Master of Philosophy Computer Science, Master of Philosophy Computer Science at Universiti Teknologi MalaysiaBachelor of Computer Science Data Science and Computational Intelligence, Bachelor of Computer Science Data Science and Computational Intelligence at International Islamic University Malaysia
Github Skills (13)
computer-vision10
pytorch10
deeplearning-ai10
machine-learning10
data-export10
exports10
deep-learning10
python10
exporter10
onnx9
tflite8
tensorrt8
coreml8
Programming languages (13)
Github contributions (5)
ultralytics/ultralytics
Aug 2023 - Apr 2025
Ultralytics YOLO11 🚀
Role in this project:
ML Engineer
ML EngineerContributions:39 reviews, 166 PRs, 286 pushes in 1 year 8 months
Contributions summary:Mohammed contributed significantly to the development and maintenance of the Ultralytics YOLO repository, focusing on improvements related to model training, export, and validation. Their work included addressing bugs in the export process for various formats (ONNX, TensorRT, TFLite, CoreML) and fixing issues related to dynamic batch sizes and NMS. They also made modifications to segmentation, keypoint, and detection validation metrics, and incorporated improvements for the handling of YOLO-NAS models.
pytorchdeep-learningyolov8object-detectiononnx
Y-T-G/community
Jan 2025 - Mar 2025
Repository containing community-contributed Ultralytics model configs.
Contributions:60 PRs, 102 pushes, 62 branches in 1 month
ultralytics
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