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Assessing your cultural and operational fit
AI Researcher and Engineer @ Netflix
I am both an AI engineer and applied researcher, and always passionate about large-scale problems including building AI products at scale. Past academic experience: I received my PhD in Computer Science, in the area of Reinforcement Learning, under supervision of Rich Sutton at University of Alberta. During my PhD I developed gradient Temporal-Difference Learning algorithms known as GTD, GQ(lambda), and greedy-GQ, etc.. Then I did a Post-Doc with Ben Van Roy at Stanford University before joining industry; a while back.
University of Alberta
PhD in RL/AI, Computer Science
January 1, 2007 – January 1, 2011
UCL
M.Phil., Machine Learning
January 1, 2003 – January 1, 2005
Brandeis University
Master's degree, Physics/Computational Neuroscience
January 1, 2003 – Present
Sharif University of Technology
Bachelor's degree, Physics
May 1, 1999 – Present
Netflix
AI Researcher and Engineer, Recommendation Algorithms
April 1, 2022 – Present
Los Gatos, California, United States
Cruise
Staff Applied Research Scientist/ML Engineer
February 1, 2020 – December 1, 2021
San Francisco Bay Area
Criteo
Staff Research Scientist (Machine Learning)
May 1, 2017 – January 1, 2020
San Francisco Bay Area
Samsung Research America
Sr Staff Research Engineer (Deep Learning)
September 1, 2016 – May 1, 2017
San Francisco Bay Area
Samsung Research America
Staff Software Engineer
February 1, 2015 – September 1, 2016
San Francisco Bay Area
Exchange Solutions Inc.
Lead Machine Learning Scientist and Engineer
July 1, 2014 – January 1, 2015
Toronto, Canada Area
Addictive Mobility
Lead Machine Learning Scientist/Engineer
December 1, 2013 – June 1, 2014
Toronto, Canada Area
Stanford University
Postdoctoral Fellow in Machine Learning
December 1, 2011 – November 1, 2013
San Francisco Bay Area
Bike Finder
August 1, 2014 – Present
Bike Finder is an SMS service that delivers live Bike Share Toronto information to customers via text message. The service takes a user’s input intersection and returns bicycle and dock availability information for the nearest Bike Share station. The winning project from HackBikeShareTO, Bike Finder went live after 1.5 days of development. Our team designed, coded, and marketed the service and is currently in implementation talks with Bike Share Toronto.
Cultural Fit Analysis
The candidate has a strong background in AI/ML research and engineering across various industries (streaming, autonomous vehicles, advertising, mobile). While the target role is 'Big Data Engineer', the candidate's experience with large-scale data pipelines and distributed ML training shows a foundational fit for handling big data. However, the primary focus has been on ML/AI application rather than core data engineering infrastructure or data warehousing, which might require some adaptation. The diverse project experience and academic background suggest an adaptable and intellectually curious individual.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight leadership roles and involvement in designing, coding, and marketing a project (Bike Finder), suggesting strong initiative and potentially good communication and collaboration skills. The focus on solving complex prediction problems in AV and optimizing customer engagement indicates a problem-solving mindset. However, without specific psychometric test results or interview data, a definitive assessment of soft skills and operational fit is limited.