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4-6 years
6-10 years
10-15 years
More than 15 years
National Taiwan University
Taipei City, Taiwan
Media Planning Assistant Manager @DAC_台灣迪艾思股份有限公司2023 ~ Present
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Account Manager
Within one month
Employed
・
Ready to interview
Full-time / Not interested in working remotely
4-6 years
National Taiwan University
・
Bachelor
Director of Operations @Jmen Taiwan - Jiuh Men 鉅門工業2020 ~ Present
・
資深產品經理 Senior Product Manager
Within one month
洪健洲 Chien Chou Hong 12 年專案管理經驗，4 年團隊主管經驗 7 年以上敏捷式軟體開發團隊 Product Manager / Product Owner / Scrum Master 經驗，專注於軟體產品的開發，負責進行用戶訪談、繪製產品線稿、制定產品規格、並協助團隊進行驗證 擅長對內與開發團隊成員整合意見，以
Employed
・
Ready to interview
Full-time / Interested in working remotely
10-15 years
National Taiwan University
・
Electronics EngineeringControls/Software Engineer @CYTENA BPS2022 ~ Present
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Software Engineer / Backend Engineer
Within one month
Employed
・
Ready to interview
Full-time / Interested in working remotely
6-10 years
National Taiwan University
・
Computer SciencePast
Team leader of Production Engineer @艾司摩爾2016 ~ 2023
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資深工程師、技術主管、專案管理工程師、產品經理
Within three months
Unemployed
・
Ready to interview
Full-time / Interested in working remotely
10-15 years
National Taiwan University
・
Mechanical Engineering
Past
Data Engineer @BUBBLEYE | We're hiring!2021 ~ 2022
・
Software Enginer
Within two months
陳昭儒(Chao-Ju Chen) Github [email protected] Education National Taiwan University Bachelor’s Degree, Electrical Engineering 2012 ~ 2017 Project Highlights Aggregating Files in one ETL, output 60B row to Data Warehouse Input :gzipped files(200GB in total) Task : Loading columns with values parsed from each gzipped file name. Wrote to BigQuery existing table(specific schema) in parallel. Tool: GCP Dataflow(Hosted Serverless Apache Beam) Result : The job took 40min to finish. Machine Type: n1-standard-1(1 vcpu, 3.75GB memory) Autoscaled up to 122 workers at peak. The data
Unemployed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
National Taiwan University
・
電機工程學系
AI Technical Lead @WeBIM Services2024 ~ Present
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前端工程師、後端工程師、全端工程師
Within one month
Preston Tseng AI Technical Lead at WeBIM Services Since high school, I have embarked on a journey of self-education in programming. My enthusiasm for technology motivated me to enroll in computer science courses throughout my university and postgraduate studies. Eager to refine my practical skills, I actively participated in internships and side-projects from my university years. Initially, I began my career as a front-end developer. However, my eagerness to expand my skill set led me to volunteer for back-end development tasks, ultimately transitioning into a full-stack developer role. I possess
Employed
・
Open to opportunities
Full-time / Interested in working remotely
4-6 years
National Taiwan University
・
Civil Engineering (Division of Transportation Engineering)
Product DEV Support Supervisor @Wiwynn2021 ~ Present
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PM/產品經理/專案管理
Within one month
王而立 Early Wang 7 years+ of experience in ODM/EMS electronic industry, familiar with notebook, desktop and server product. Windchill PLM operation & implementation with 4+ years of experience Agile PLM operation & implementation with 2+years of experience Team work, good communication with cross-function, requirement analysis and product integration. Taipei City, Taiwan Email: [email protected] WORKING EXPERIENCE Product DEV Support Supervisor • Wiwynn Corp. MarAgile PLM implementation & maintenance - In charge of Component/BOM/Project module process planning, requirement interview and analysis, cross function coordinate, system testing, and
Employed
・
Open to opportunities
Full-time / Interested in working remotely
4-6 years
National Taiwan University
・
圖書資訊學系
Mobile Architect @Rakuten2019 ~ Present
・
Senior Android Engineer
Within one month
Rubio Huang Mobile Architect Tokyo, Japan [email protected] Summary 10 years of professional experience in software engineering. Proficiency in Android development platforms. A self-motivated person with superior communication and organization skill. Enjoy working in a global company. Lead with creativity and confidence. Work Experience Rakuten Inc Mobile Architect Apr 2019 ~ Present Role and Responsibility - Pursue high quality and performance by designing the scalable application architecture. - Stay up to date with modern trends, technologies, frameworks and follow best practices. - Host monthly meetup to share Android knowledge among all the mobile
Employed
・
Not open to opportunities
Full-time / Interested in working remotely
6-10 years
National Taiwan University
・
Master degree Computer Science
Consultant @TATA Consultancy Service (TCS)2023 ~ Present
・
Senior Software Engineer
Within one month
— Jingping Wang 王景平 More than 15+ years of experience in software development. Projects participated include from large scale insurance/banking web applications to mobile applications. In recent years, I have been engaged in software defined networking and 4G/5G user data plane networking. Taipei City, Taiwan Phone:E-Mail: jingping.w＠tcs.com EXPERIENCE Full Stack Engineer • TATA Consultancy Services (TCS) JanPresent TaiShin Bank Richart App Frontend / 台新 Richart 數位銀行 TaiShin Bank Richart App Backend / 台新 Richart 數位銀行 TaiShin
Employed
Full-time / Interested in working remotely
More than 15 years
National Taiwan University
・
Atmospheric Sciences
Backend Engineer @Cooby2022 ~ 2023
・
Within one month
Conrad Lan Da’an District, Taipei City, [email protected] https://www.linkedin.com/in/conrad-lana4/ ▶ three+ years of backend engineer in python/golang 工作經歷 Backend Engineer • Cooby 四月八月 2023 | Taipei, Taiwan I am a backend engineer with a track record of designing and implementing robust, scalable, and cost-effective infrastructure solutions for web applications. My core competencies include: - AWS Infrastructure: Successfully migrated the Cooby backend infrastructure from Heroku to AWS, establishing a unified management system using Terraform
Employed
・
Not open to opportunities
Full-time / Interested in working remotely
4-6 years
National Taiwan University
・
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Fields of Employment
Big Data, Artificial Intelligence / Machine Learning, Semiconductor
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Job types
Full-time
Locations
Remote
Interested in working remotely
Freelance
Educations
School
National Taiwan University
Major
Mechanical Engineering
Kao Chiang
NTU ME
#ML #NLP #Recommender
Skills
Software
> ML: Tensorflow, Keras, Xgboost,
LightGBM, Pytorch
> NLP: Rasa, spaCy, Gensim, GluonNLP, GloVe
> Web: Flask, Django, Vue, SQL
> APP: Swift, Android Studio
> Others: Docker, docker-compose, Spark
Combination
There are lots of open source tools and platforms to help us to develop the project, but how to combine each tool or platform well (efficiency & security) is the point.
With some experience of organizing an whole project by myself, I am good at making good use of the newest technology to solve the problems.
Communication
Nowadays, it is quite easy to use many powerful tools, but mostly each of them are used in different platform or languange, and each of tools often include many knowledge which need to be considered various aspects. In some experience of system design, I
Projects
Chatbot
An end-to-end chatbot platform which includes two part of main services. One is a friendly interface to edit corpus and dictionary for training machine-learning model; Another platform is a chatbot services include chat room for testing , logging for remarking incorrect response, and so on. Most of NLP models are applied in English or western language, but our clients are Chinese. So, I need to re-write many program flow to be suitable and well on Chinese .Face Recognition
In a corporation with a security company, they want to include some AI in their security system in an exhibition. There are two main of conditions. One is to apply in department store to recognize and record the flow of people with gender and age instantly. The other one is an access control by recognize face of people.
To their demand, we make a device embedding two machine learning model to detect face and recognize age and gender.
Recommender System
It is lucky to participate the design of recommender system of a top e-commerce platform, and that is the first time I took "big data". Because of the amount of data, the data pipeline need to be very careful in the parallel computing. We use Apache Spark framework and Kubernetes to deploy our models.
The recommender system is mainly combined by two models. One model is user-based model and the other is content-based model. We use fully-connected layer to combine.
Kao Chiang
NTU ME
#ML #NLP #Recommender
Skills
Software
> ML: Tensorflow, Keras, Xgboost,
LightGBM, Pytorch
> NLP: Rasa, spaCy, Gensim, GluonNLP, GloVe
> Web: Flask, Django, Vue, SQL
> APP: Swift, Android Studio
> Others: Docker, docker-compose, Spark
Combination
There are lots of open source tools and platforms to help us to develop the project, but how to combine each tool or platform well (efficiency & security) is the point.
With some experience of organizing an whole project by myself, I am good at making good use of the newest technology to solve the problems.
Communication
Nowadays, it is quite easy to use many powerful tools, but mostly each of them are used in different platform or languange, and each of tools often include many knowledge which need to be considered various aspects. In some experience of system design, I
Projects
Chatbot
An end-to-end chatbot platform which includes two part of main services. One is a friendly interface to edit corpus and dictionary for training machine-learning model; Another platform is a chatbot services include chat room for testing , logging for remarking incorrect response, and so on. Most of NLP models are applied in English or western language, but our clients are Chinese. So, I need to re-write many program flow to be suitable and well on Chinese .Face Recognition
In a corporation with a security company, they want to include some AI in their security system in an exhibition. There are two main of conditions. One is to apply in department store to recognize and record the flow of people with gender and age instantly. The other one is an access control by recognize face of people.
To their demand, we make a device embedding two machine learning model to detect face and recognize age and gender.
Recommender System
It is lucky to participate the design of recommender system of a top e-commerce platform, and that is the first time I took "big data". Because of the amount of data, the data pipeline need to be very careful in the parallel computing. We use Apache Spark framework and Kubernetes to deploy our models.
The recommender system is mainly combined by two models. One model is user-based model and the other is content-based model. We use fully-connected layer to combine.