Mohsen Moslehpour
FRL Staff Research Scientist at Meta
San Jose, California, United States
Summary
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Rockstar🎓
Top SchoolMohsen Moslehpour is a FRL Staff Research Scientist at Meta with a decade of experience building production-grade ML systems that bridge research and product, currently leading efforts to bring multimodal LLMs and visual-text interactions to smart glasses. His background spans autonomous vehicle perception at Cruise and algorithmic ad optimization at Yahoo and AOL, reflecting deep expertise in signal processing, estimation, and applied deep learning. He contributed to Facebook Research’s fairseq by making core components more scriptable and deployable, showing a practical focus on model portability and ONNX compatibility. Holding a PhD in Electrical and Computer Engineering, Mohsen blends rigorous academic research on physiological signal modeling with hands-on engineering to deliver end-to-end products from design to final delivery. Colocated in San Jose, he’s comfortable shipping systems across hardware-constrained platforms, an asset for edge AI and wearable innovation.
10 years of coding experience
10 years of employment as a software developer
Amirkabir University of TechnologyAlborz High SchoolDoctor of Philosophy (Ph.D.), Electrical and Computer Engineering, Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering at Michigan State University
English, Persian
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
pytorch10
machine-learning10
python10
fairseq10
onnx9
nlp8
artificial-intelligence8
natural-language-processing8
Programming languages (2)
Github contributions (4)
facebookresearch/fairseq
Oct 2022 - Oct 2022
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Role in this project:
ML Engineer
ML EngineerContributions:6 reviews, 7 commits, 16 PRs in 7 days
Contributions summary:Mohsen primarily focused on making various components within the fairseq toolkit scriptable, which includes dynamic convolutions, multihead attention, and lightconv layers. They addressed the onnx export compatibility of sinusoidal positional embedding. Their contributions also involved fixing bugs and adapting models to be more readily deployable. The user's work indicates a focus on improving the usability and integration of the model components.
aipythonsequence-to-sequencepytorchartificial-intelligence
moslehpour/My-Programming-Challenges
Jan 2016 - Jan 2016
Contributions:1 push, 1 branch in 1 day
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