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Machine Learning Software Engineer at google
Objectives: - Solve hard problems to make the world a better place. - SWE elegant solutions & Algorithms that can scale. - Redefine whats possible through Machine Learning & AI solutions. - Weild the power of LLMs & Transformers into real world applications. - Learn something new everyday. Specialties: Academic and industry experience in software engineering, machine learning, data mining, statistics, deep NN, boosted trees, numerical optimization, graphical models, causality inference, transformers and large language models (LLMs). Experience in software engineering and development using different languages and platforms.
University of Louisville
Ph.D., Computer Science and Engineering
January 1, 2007 – January 1, 2010
University of Louisville
M. Sc., Computer Engineering and Computer Science
January 1, 2005 – January 1, 2007
Machine Learning Software Engineer
September 1, 2018 – Present
Mountain View, California · On-site
ML Software Engineer
March 1, 2017 – September 1, 2018
Data Scientist
November 1, 2015 – March 1, 2017
ID Analytics
Senior Data Scientist
November 1, 2011 – October 1, 2015
San Diego, CA, USA
Weill Cornell Medical College
PostDoc
January 1, 2010 – November 1, 2011
University of Louisville
Lecturer / Teaching Assistant / Research Assistant
January 1, 2005 – January 1, 2010
Palestine Cellular Communication LTD (JAWWAL)
Software Engineer
January 1, 2004 – January 1, 2005
Cultural Fit Analysis
The candidate has a diverse background spanning academia, research, and industry, including roles at major tech companies. The progression from Data Scientist to ML Software Engineer and Tech Lead demonstrates adaptability and a continuous learning mindset. The breadth of experience in different domains (search, ads, fraud detection, bioinformatics) suggests a versatile individual who can contribute to various projects. However, the target role is 'Data Analyst', which might be a step down from their current 'Machine Learning Software Engineer' and 'Tech Lead' roles, potentially indicating a mismatch in career trajectory or expectations.
Soft Skills & Operational Fit
The candidate's experience as a Tech Lead and working with multifunctional teams suggests strong leadership, collaboration, and problem-solving skills. The academic background and research experience indicate a strong analytical mindset and ability to handle complex challenges. However, specific details on communication style, stress handling, or team collaboration are not available from the provided data.