Machine Learning Engineer at Snapchat
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Assessing your cultural and operational fit
(1) 10+ years deep expertise in large-scale machine learning (ML) model development: Gen AI Safety and Security, On-device large language model (LLM), natural language understanding, knowledge graph, recommendation, safety and integrity. (2) Tech Lead of a team comprising senior/junior engineers, human annotators, project manager, data scientist, and user experience researcher. Applying ML techniques to improve user experience and grow daily active users. Mentoring and growing junior engineers.
University of Notre Dame
Doctor of Philosophy (Ph.D.), Electrical and Electronics Engineering
N/A – Present
Harbin Institute of Technology
Bachelor of Engineering (BEng), Electrical, Electronics and Communications Engineering
N/A – Present
Snap Inc.
Machine Learning Engineer
September 1, 2025 – Present
Los Angeles, California, United States · On-site
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Cultural Fit Analysis
The candidate's background is heavily focused on Machine Learning Engineering and Research Scientist roles, with a strong emphasis on LLMs, recommendation systems, and fraud detection. While these skills are highly technical, the target role is 'Data Analyst'. This represents a significant mismatch in primary function and day-to-day responsibilities. The candidate's experience is geared towards model development, research, and deployment, rather than data analysis, reporting, and business intelligence, which are typical for a Data Analyst role. The project diversity is strong within the ML domain, but not aligned with the target role's core competencies.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight leadership in technical projects and a focus on measurable impact, suggesting strong problem-solving and results-orientation. The nature of their roles at Meta and Snap Inc. implies collaboration within large engineering teams. However, specific soft skill assessments are not available.