Welcome to

Hamed's Webpage

Machine Learning and AI

Hamed Firooz

I have over 12 years of experience in shaping and delivering large-scale AI solutions for various products with a track record of developing and leading multi-year technology strategies. Over 7 years of experience managing research and engineering teams across multiple sites

  • Current position: Principle Staff AI Scientist at LinkedIn Core AI
  • Education: PhD from University of Washington (UW)

Education

  • 2012
    PhD
    University of Washington

    Compressed Sensing and Network Coding

  • 2008
    MSc
    University of Tehran

    Peer-to-peer networks

Experience

  • 2023 - Current
    Principle Staff AI Scientist
    LinkedIn Core AI

    I have formed and currently lead a team of over 20 AI scientists and engineers to train and opera- tionalize an LLM-based foundational model for LinkedIn’s personalization tasks at scale.

  • 2018 - 2023
    Sr. Staff AI Tech Lead Manager
    Meta AI

    Leading a medium size team with diverse profiles, research scientists and software engineers. Our mission is to advance AI technologies to keep users safe online. My team builds multimodal content understanding services used across many Meta integrity products.

  • 2016 - 2018
    Staff Machine Learning Engineer
    LinkedIn

    Leading LinkedIn Ads Sponsored Update relevance (five engineers, one analytics, one PM). The team is responsible for modeling and raking advertising content on the LinkedIn news feed that shows ads from millions of advertisers to hundreds of millions of Linkedin daily active users.

  • 2015 - 2016
    Staff Machine Learning Tech lead Manager
    Base CRM (acquired by Zendesk)

    Leading a four-engineer group for forecasting. We are responsible for a) Predicting sales attributes (dollar amount, closed date, and the closing probability) for the Sales team b) Predicting the possibility of churn for the Customer Success (CSM) team. media coverage

  • 2012 - 2015
    Senior Machine Learning Engineer
    Falkonry (acquired by IFS)

    Building an early warning system based on the Bayesian network that provides diagnosis and prognosis of large industrial machines.

2024 Highlights
Contact