Shreyas Joshi
Staff Machine Learning Engineer at Apple
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolShreyas Joshi is a Staff Machine Learning Engineer at Apple with a decade of experience building production-ready computer vision and AR/VR systems, bridging research and productization. Trained at Carnegie Mellon in computer vision, he has advanced multimodal sensing, image-to-image technologies, and 3D vision across Apple’s VE/VCV teams while managing projects from big data pipelines to deployed CoreML models. He contributed to the widely used open-source Turi Create—improving style transfer, CoreML export, and data augmentation—demonstrating attention to both model quality and developer ergonomics. Known for combining rigorous research with pragmatic engineering, he also taught robotics math at CMU and has a publication record reflecting applied vision research. Based in Seattle, he brings deep expertise in style transfer and AR/VR sensing that informs scalable, product-focused ML solutions.
10 years of coding experience
12 years of employment as a software developer
BMEMHS, DharwadMaster's degree (MSCV) Computer Vision, Master's degree (MSCV) Computer Vision at Carnegie Mellon UniversityBachelor of Engineering (BE) Electrical Electronics and Communications Engineering, Bachelor of Engineering (BE) Electrical Electronics and Communications Engineering at BVBCET
English, Hindi, Kannada
Github Skills (10)
machine-learning10
deep-learning10
python10
style-transfer10
coreml10
exporter9
data-export9
exports9
mxnet8
api-documentation7
Programming languages (1)
Github contributions (5)
apple/turicreate
Jun 2018 - Aug 2018
Turi Create simplifies the development of custom machine learning models.
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 10 PRs, 2 pushes in 2 months
Contributions summary:Shreyas primarily contributed to the style transfer functionality within the Turi Create library. Their commits include fixing CoreML export issues, enabling data augmentation, and allowing flexible input shapes for CoreML models. They also addressed data iteration issues and added documentation for the style transfer API, demonstrating a focus on improving the model's usability, performance, and documentation.
pythondeep-learningmachine-learningcoremlmulticlass-classification
shreyasvj25/caffe-utils
May 2016 - Jan 2020
Contributions:9 pushes, 1 branch in 3 years 9 months
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