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Software Development Engineer, Software Developer in Machine Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Software Engineer | Software Engineer in Machine Learning
Carnegie Mellon University
Master of Science (M.S.), Computer Science
January 1, 2016 – January 1, 2018
Korea Advanced Institute of Science and Technology
Exchanged Student, Electrical Engineering
January 1, 2015 – January 1, 2015
Harbin Institute of Technology
Bachelor of Engineering (B.E.), Electrical, Electronics and Communications Engineering
January 1, 2012 – January 1, 2016
Software Engineer
April 1, 2024 – Present
YouTube
Software Engineer
May 1, 2022 – April 1, 2024
Microsoft
Software Engineer
May 1, 2020 – April 1, 2022
Redmond, Washington, United States
Boeing
Machine Learning Engineer
July 1, 2018 – May 1, 2020
Greater Seattle Area
Carnegie Mellon University
Graduate TA for 11695(Competitive Engineering: Deep Learning Algorithm with Tensorflow)
February 1, 2018 – May 1, 2018
Greater Pittsburgh Area
Qubole
Software Engineer Internship
May 1, 2017 – August 1, 2017
Santa Clara, CA
Harbin Institute of Technology
Research Assistant
July 1, 2015 – July 1, 2016
HIT
Cultural Fit Analysis
The candidate has a strong background in large, reputable tech companies (Google, YouTube, Microsoft) and a research-oriented role at Boeing, indicating adaptability to diverse corporate and R&D environments. The transition from Software Engineer to roles with significant ML/AI focus aligns with a data-driven culture. However, the target role is 'Data Analyst' while the experience is heavily skewed towards 'Software Engineer' and 'Machine Learning Engineer', which might indicate a mismatch in day-to-day responsibilities and expectations for a pure data analyst role. The breadth of skills is strong in ML/AI engineering but less explicit in traditional data analysis tools and methodologies.
Soft Skills & Operational Fit
The candidate's resume highlights experience in driving MLPerf Inference Datacenter submissions and developing entire pipelines, suggesting strong project ownership and execution skills. Experience as a Graduate TA indicates mentoring and communication abilities. However, without psychometric or English test scores, a comprehensive assessment of soft skills and operational fit is limited.