Lara Haidar-ahmad
Machine Learning Engineer at Hinge
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolLara Haidar-ahmad is a Machine Learning Engineer with 11 years of experience building production-ready ML systems across industry leaders including Hinge, Bloomberg, and Microsoft, based in New York. She specializes in model interoperability and deployment, contributing to high-profile open-source projects like ONNX and PyTorch Vision—adding operators, improving export paths, and ensuring Windows build stability for complex CV models. Her background in semantic web and NLP from Polytechnique Montréal complements a strong engineering foundation in computer engineering, enabling her to bridge research-oriented models and robust engineering practices. Notably, she has hands-on experience making advanced vision models (Faster R-CNN, Mask R-CNN, keypoint R-CNN) portable via ONNX Runtime, a skill that reduces friction from prototype to production.
11 years of coding experience
8 years of employment as a software developer
Master’s Degree Semantic Web and Natural Language Processing, Master’s Degree Semantic Web and Natural Language Processing at Polytechnique Montréal
English, French, Arabic
Github Skills (21)
pytorch10
c-language10
learn-ruby-on-rails10
python10
user-manual10
machine-learning10
data-export10
onnx10
basics10
exporter10
deep-learning10
onnxruntime10
computer-vision10
exports10
cprogramming-language10
Programming languages (4)
Github contributions (5)
onnx/onnx
Mar 2019 - Apr 2020
Open standard for machine learning interoperability
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 14 PRs, 90 comments in 1 year
Contributions summary:Lara primarily focused on enhancing the onnx/onnx repository by adding support for new features within existing operators like MaxPool and AveragePool, specifically related to ceiling mode. They also implemented new operators such as GatherElements and updated existing ones, like ScatterElements, demonstrating an understanding of the library's core functionality and its interface for various machine learning operations. The user's work involved modifying operator definitions, shape inference, and tests, indicative of a role focused on extending ONNX's capabilities.
pytorchmxnetdeep-learninginteroperabilitymachine-learning
pytorch/vision
Sep 2019 - Jan 2020
Datasets, Transforms and Models specific to Computer Vision
Role in this project:ML Engineer
ML EngineerContributions:15 commits, 14 PRs, 54 comments in 4 months
Contributions summary:Lara primarily focused on integrating custom operations within the PyTorch vision library to support ONNX export functionality. Their contributions involved registering Torchvision ops as custom ops, implementing and modifying existing operations such as RoIAlign and NMS to work with ONNX. They also addressed build issues, Windows build fixes and added support for exporting other models like faster RCNN, mask rcnn, and keypoint rcnn.
pytorchvisiondeep-learningdatasetcomputer-vision
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