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Lead Machine Learning Engineer at SharkNinja | PhD in CS
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Michigan State University
Ph.D, Computer Science
January 1, 2012 – January 1, 2017
Xi'an Jiaotong University
Master of Science, Computer Science
January 1, 2009 – January 1, 2012
Xi'an Jiaotong University
Bachelor's degree, Software Engineering
January 1, 2004 – January 1, 2008
SharkNinja
Lead Machine Learning Engineer
March 1, 2026 – Present
San Diego, California, United States · Remote
Cotality
Principal Machine Learning Scientist
June 1, 2025 – March 1, 2026
San Diego, California, United States · Hybrid
Cotality
Senior Machine Learning Scientist
August 1, 2023 – June 1, 2025
San Diego, California, United States · Hybrid
Chenguoniren & NWPU
Principal Scientist (Deep Learning, AI)
March 1, 2021 – June 1, 2023
China
Walmart Global Tech
Senior Software Engineer (Machine Learning, NLP)
January 1, 2019 – January 1, 2021
United States
Samsung Electronics America
Senior Software Engineer
January 1, 2017 – January 1, 2019
United States
Ask Sam - Conversational AI Assistant for Associates and Members of Sam’s Club
January 1, 2020 – January 1, 2021
• Designed and implemented Sam’s Club voice assistant for 600+ clubs across USA and 1M+ online members. • Researched and improved intent classification and ner/ned via BERT & Transformers. • Implemented end-to-end pipeline for continuous annotation/training/deployment on MS Azure K8s & AutoML. • Reached > 95% online accuracies for understanding the utterances of Sam’s members and associates across USA.
App Recommendation System for Samsung Smartphone
December 1, 2017 – January 1, 2019
➢ Designed and developed application recommendation solution based on smartphone users' behaviors. ➢ Used Tensorflow Lite to implement on device classification to improve Samsung Bixby's recommendation. ➢ Leveraged SVM, CNN, LSTM and KNN algorithms to recommend app for Android mobile device. ➢ Explored Python Flask (RESTful API) on Google Cloud Platform to ensemble on device classifiers.
Walk and Learn: Indoor Localization via Learning Outdoor Motion Behaviors
January 1, 2015 – April 1, 2017
➢ Leveraged human bodies' motion to build a decimeter-level indoor localization application. ➢ Combined dead reckoning approach and Markov chain to generate a user's motion trace. ➢ Enhanced the location accuracy by pro ling and learning outdoor motions of 15 volunteers. ➢ Applied Transfer Learning on users' data sets and implemented the prototype on Android OS.
Cultural Fit Analysis
The candidate has a diverse background spanning large corporations (Samsung, Walmart), startups (Cotality), and academic institutions (Chenguoniren & NWPU), indicating adaptability to different organizational cultures. The progression through Senior, Principal, and Lead roles demonstrates ambition and a commitment to growth. The projects showcase a breadth of applications from recommendation systems and indoor localization to conversational AI and multimodal search, aligning well with the diverse problem-solving nature often found in ML Engineer roles. The academic background (Ph.D.) also suggests a strong research-oriented approach which can be beneficial for innovation.
Soft Skills & Operational Fit
The candidate's experience as a Lead Machine Learning Engineer and Principal Machine Learning Scientist, along with leading a research team, suggests strong leadership, project management, and mentorship abilities. The descriptions of designing and architecting solutions imply strong problem-solving and strategic thinking. The involvement in hackathons and innovation awards indicates a proactive and innovative mindset. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.