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Software Engineer at YouTube
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
My skillset merges machine learning research experience (LLMs, recommender systems, cognitive modeling) and foundational ML knowledge with practical software engineering skills (including, but not limited to, machine learning engineering). I am looking for a role which requires a lot of learning/ growth, on a product that benefits society at large.
UC San Diego
Master’s Degree, Computer Science
January 1, 2016 – January 1, 2017
University of California, San Diego
Bachelor’s Degree, Computer Science
January 1, 2012 – January 1, 2016
Irvine High School
High School
January 1, 2008 – January 1, 2012
YouTube
Senior Software Engineer
April 1, 2018 – Present
New York, New York, United States
Padhraic Smyth Lab
NLP Researcher
September 1, 2017 – May 1, 2018
Irvine, CA
Julian McAuley Lab
Recommender Systems Researcher
July 1, 2017 – April 1, 2018
La Jolla, California
HP Labs - Analytics Lab
Research Associate (Deep Learning Lab)
June 1, 2015 – September 1, 2015
Cottrell Lab
Neural Networks Researcher
September 1, 2014 – June 1, 2017
University of California, San Diego
CSE Tutor
January 1, 2014 – December 1, 2015
Cultural Fit Analysis
The candidate has a strong background in academic research and industry roles focused on ML. The diversity of research projects (NLP, Recommender Systems, Deep Learning, Neural Networks) indicates adaptability and a broad interest in the ML domain. The transition from research to production ML at YouTube, and then to core software engineering, suggests a versatile profile. However, the current role description at YouTube (2020-present: 'more core software engineering/ computational analysis work to improve data quality') might indicate a shift away from direct ML model development, which could be a slight misalignment with a pure ML Engineer role if the target role is heavily focused on model building and deployment.
Soft Skills & Operational Fit
The candidate's experience descriptions suggest a strong research-oriented mindset and the ability to work on complex, interdisciplinary projects. The role at YouTube indicates experience in production environments, which is crucial for an ML Engineer. However, without specific project details or direct communication assessment, it's difficult to fully evaluate operational fit beyond technical contributions.