
Lead Machine Learning Engineer at SharkNinja | PhD in CS
AI is analyzing your overall score…
Identifying your key strengths…
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 proling 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.