Nazir Saleheen
Machine Learning Engineer at Meta
San Jose, California, United States
Summary
👤
Senior🎓
Top SchoolNazir Saleheen is a Machine Learning Engineer with a decade of experience building large-scale, production ML systems and academic-grade computational models for health behavior detection from noisy wearable sensors. He combines deep research credentials—PhD work analyzing 350,000+ hours (250 billion data points) across 650+ participants and publications in top venues—with hands-on production experience at Google and Meta, where he engineered pipelines processing hundreds of billions of daily data points for security and privacy applications. Nazir’s work spans end-to-end system design, from study protocol and IRB approvals to mobile data collection, modeling (CNNs, LSTMs, autoencoders) and deployment of tools like puffMarker used in multi-institution smoking cessation studies. He also pioneers methods to quantify and protect behavioral privacy in time-series sensor sharing, bridging ML-for-health innovations with responsible data practices. Based in San Jose, he pairs strong software skills (C/C++, Java, Python, Android) with grant-supported interdisciplinary collaborations and a track record of translating messy real-world signals into reliable, actionable insights.
10 years of coding experience
13 years of employment as a software developer
Master's degree, Mathematics, Master's degree, Mathematics at University of MemphisMaster of Science - MS, Computer Engineering, Master of Science - MS, Computer Engineering at University of Dhaka
Github Skills (16)
stream7
real-time-data6
api5
data-stream3
backend3
architecture2
stream-processor2
mhealth2
cortex2
spark2
scale2
apache-spark1
machine-learning1
apache1
configuration-file1
Programming languages (2)
Github contributions (5)
nsaleheen/CerebralCortex-DataAnalysis
Jan 2018 - Mar 2018
Contributions:19 pushes in 2 months
nsaleheen/stream-processor
Mar 2017 - Feb 2018
Contributions:2 pushes, 1 branch in 10 months
real-time-dataautosensedata-streamstream-processorstream
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