Yu-Yang Huang
ML EngineerMachine Learning SWE at Google
谷歌
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Machine Learning SWE at Google
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Machine Learning Engineer | Design and deliver ML solutions to complex problems. Work experience in NLP, computer vision, ads and recommendation systems. Skilled in building deep learning models and optimizing data pipelines and experiments.
National Taiwan University
Master's Degree, Computer Science and Information Engineering
January 1, 2013 – January 1, 2015
National Taiwan University
Bachelor's Degree, Electrical Engineering
January 1, 2009 – January 1, 2013
谷歌
Senior Software Engineer, Machine Learning
June 1, 2024 – Present
Banqiao District, New Taipei City, Taiwan · Hybrid
Microsoft
Applied Scientist II
November 1, 2021 – June 1, 2024
Taipei City, Taiwan · Hybrid
Taiwan AILabs
Algorithm Engineer
July 1, 2019 – October 1, 2021
Taipei City, Taipei City, Taiwan · Hybrid
Appier
Senior Scientist, Machine Learning
January 1, 2019 – May 1, 2019
Taipei City, Taipei City, Taiwan · On-site
Appier
Machine Learning Scientist
November 1, 2016 – January 1, 2019
Taipei City, Taipei City, Taiwan · On-site
keras-inception-resnet-v2
July 1, 2017 – September 1, 2017
This project implements an Inception-ResNet v2 model using Keras, with pre-trained network weights converted from the official TensorFlow-Slim release into Keras format. Before this project, there were a few open source Keras implementations available. However, none of them has provided pre-trained model weights, which severely limited the applicability of these models. This repository contains scripts to download and convert the weights in order to reproduce the reported accuracy on the ImageNet validation set. This model was later merged into the Keras codebase in the 2.0.9 release.
Cultural Fit Analysis
The candidate has worked in diverse environments, from large corporations (Google, Microsoft) to AI labs and startups (Taiwan AILabs, Appier). This breadth of experience suggests adaptability. The personal project contributing to Keras codebase indicates a collaborative and open-source mindset. The target role of ML Engineer aligns well with the candidate's extensive experience in ML model development, deployment, and optimization.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving approach and a focus on practical application of advanced ML techniques. Experience in collaborating with various teams (News, Speech, Language Understanding) suggests good teamwork and communication skills. The personal project demonstrates initiative and a deep understanding of ML frameworks.