
Software engineer at Google
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Georgia Institute of Technology
Master's degree, Computer Science
January 1, 2014 – January 1, 2016
Arizona State University
Master of Science (M.S.)
January 1, 2008 – January 1, 2011
Zhejiang University
Bachelor of Science (B.S.)
January 1, 2002 – January 1, 2006
Senior Software Engineer
July 1, 2025 – Present
Sr Software Engineer
June 1, 2022 – June 1, 2025
Waymo
Senior Software Engineer
May 1, 2020 – July 1, 2022
Software Engineer
August 1, 2017 – May 1, 2020
San Francisco Bay Area
ClearSlide
Software Engineer(machine learning/infrastructure)
June 1, 2015 – August 1, 2017
San Francisco Bay Area
Proximiant
Software Development Engineer(backend/data/machine learning)
February 1, 2014 – June 1, 2015
San Francisco Bay Area
SMILE Lab@Northeastern University
joint research paticipator
October 1, 2013 – February 1, 2014
GLOBALFOUNDRIES
Sr. technology development engineer
August 1, 2011 – August 1, 2013
Malta, NY
LEAN and Six-sigma green belt
Globalfoundries
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career trajectory shows a consistent focus on Machine Learning and data-intensive roles across various companies, including startups and major tech giants. This demonstrates adaptability and a broad understanding of different operational scales. The progression from Software Engineer to Senior Software Engineer at Google and Waymo suggests a strong work ethic and ability to grow within an organization. The diverse experience in ML applications (ads, YouTube, autonomous driving, predictive analytics, NLP) indicates a broad interest and ability to contribute to various ML domains, aligning well with a dynamic ML engineering culture. The multiple master's degrees also suggest a commitment to continuous learning.
Soft Skills & Operational Fit
The candidate's resume highlights roles in large, complex organizations like Google and Waymo, suggesting experience in collaborative environments and handling significant technical challenges. The description of building a 'first predictive analytics data pipeline for production' and 'Kafka cluster' at ClearSlide indicates strong operational and system design capabilities. However, without specific project details or behavioral assessment data, a comprehensive evaluation of soft skills and operational fit is limited.