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Engineering Manager at Meta BizAI
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
- Support a highly-impact ML engineering team. - 10+ years engineering experience in machine learning and recommendation.
University at Buffalo
Ph.D., Machine Learning, Information Retrieval
January 1, 2006 – January 1, 2011
Nanjing University
Master, Computer Science
January 1, 2003 – January 1, 2006
Nanjing University
Bachelor, Computer Science
January 1, 1998 – January 1, 2002
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January 1, 2026 – Present
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Staff Machine Learning Engineer
May 1, 2017 – July 1, 2021
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March 1, 2013 – May 1, 2017
San Francisco Bay Area
Amazon.com
Software Dev Engineer
September 1, 2011 – March 1, 2013
Center of Excellence for Document Analysis & Recognition, University at Buffalo
Research Assistant
September 1, 2006 – August 1, 2011
Department of Computer Science, Hong Kong Baptist University
Visiting Research Fellow
October 1, 2005 – April 1, 2006
Cultural Fit Analysis
The candidate has worked at several of the largest and most innovative tech companies, indicating an ability to thrive in fast-paced, high-performance environments. Their experience spans various domains within ML and data, suggesting adaptability and a broad interest in technical challenges. However, the target role is 'Big Data Engineer' while recent experience is heavily focused on 'Software Engineering Manager' and 'Machine Learning Engineer' roles. While there's overlap, a direct cultural fit for a pure Big Data Engineering role might require further validation of hands-on data pipeline and infrastructure expertise.
Soft Skills & Operational Fit
The candidate's career progression from individual contributor to engineering manager at major tech companies suggests strong leadership, project management, and team collaboration skills. Experience in supporting various ML teams implies an ability to adapt to different problem domains and operational contexts. The descriptions, while brief, indicate a focus on impact and system performance.