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Machine Learning Engineer at Checkr
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I dream big, modularize, reuse, work hard, read, and am always learning.
San Francisco State University
Masters, Computer Science
January 1, 2009 – January 1, 2011
UC San Diego
Bachelor's degree, Management Science / Economics
January 1, 2003 – January 1, 2007
Checkr, Inc.
Staff Machine Learning Engineer
December 1, 2018 – Present
San Francisco, California
eero
Machine Learning / Software Engineer
April 1, 2016 – December 1, 2018
San Francisco, California
Jut, Inc.
Software Engineer
October 1, 2014 – March 1, 2016
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Apple
Software Engineer
August 1, 2014 – September 1, 2014
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Beats Music
Software Engineer
April 1, 2014 – July 1, 2014
San Francisco Bay Area
Ideapod
Software Engineer (Consultant Night/Weekend)
July 1, 2013 – December 1, 2013
Monsoon Company
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February 1, 2013 – January 1, 2014
Jixee
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San Francisco, CA
Credit.com
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January 1, 2011 – August 1, 2011
Root Music
Software Engineering Intern
June 1, 2010 – August 1, 2010
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VirtuOz
Content Manager
September 1, 2008 – February 1, 2009
Lockheed Martin
Procurement Asc
July 1, 2007 – August 1, 2008
Cultural Fit Analysis
The candidate has a diverse background working at various companies from startups to large corporations (Apple, Checkr, eero). Their experience spans different domains like background checks, smart home devices, music recommendations, and real-time collaboration platforms. This breadth of experience suggests adaptability and a willingness to tackle varied challenges, which could contribute positively to cultural fit. However, the recent focus on ML Engineering might require a clear understanding of their interest in a pure Backend Engineer role.
Soft Skills & Operational Fit
The candidate's resume indicates strong problem-solving skills through designing and implementing complex ML systems. Leadership and mentoring abilities are evident from leading projects and onboarding data scientists. The detailed descriptions suggest good communication of technical concepts, which is crucial for operational fit.