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On
4-6 years
6-10 years
10-15 years
More than 15 years
Taiwan
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Within one month
reducing parameter training volume to 2% , decreasing memory usage by 70%, and increasing training speed by 25%. Architecture Optimization: Used Direct Preference Optimization (DPO) to optimize Reinforcement Learning, improving model efficiency and decision quality. [ G itHub ] Graduate Research Assistant OctOct 2023 Institute of Information Science, Academia Sinica, Taiwan Natural Language and Knowledge Processing Lab (NLP Lab) Publication: Published in ACM CIKMSequential Text-based Knowledge Update with Self-Supervised Learning for Generative Language Models . [ Paper | GitHub ] Performance has improved by 18.8% compared to traditional methods in the past and surpassed SOTA LLMs by 39.6
Employed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
國立政治大學（National Chengchi University）
・
資訊科學系
Research & Development Engineer @三竹資訊股份有限公司2023 ~ Present
・
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Within one month
趙啟雲 chiyun chao A software engineer with 4 years of experience in AI model training and backend development. In the domain of machine/deep learning, I am familiar with: - focus on NLP(Natural language processing) and DPI(Deep packet inspection) - NLP networks such as Transformer, BERT, T5 - have experience with AI training platform development with mlflow, Label Studio, DVC In terms of backend development, I have the following experience： - backend framework: Spring boot, Flask - familiar with DB syntax for Elasticsearch, PosgresSQL and MSSQL Taiwan E-mail: [email protected] Skill languages Python JAVA backend
Employed
・
Open to opportunities
Full-time / Not interested in working remotely
4-6 years
國立中央大學 National Central University
・
資訊工程
商業分析師 @凱基商業銀行股份有限公司2021 ~ Present
・
Data Analyst、Data Engineer、Data Scientist、Customer Experience Analyst
Within one month
Employed
・
Open to opportunities
Full-time / Interested in working remotely
4-6 years
國立政治大學（National Chengchi University）
・
數位內容
Android Team Lead @104 Corporation2018 ~ Present
・
Senior Software Engineer, Principal Software Engineer, Engineering Manager
Within two months
incorporating: - A clean architectural approach - Integration with Firebase services - Support for the latest Android features ensuring scalability for future organizational growth - Successfully retained a 4.6-star user rating and boosted job application rates by 10%. • AI Implementation: Created an AI tool to convert UI test cases from natural language into UiAutomator2 scripts, seamlessly integrating them into the CICD process. This resulted in a 90% acceleration UiAutomator2 test case generation. • Technology Introduction: - Flutter: Developed an in-house App Dashboard to consolidate Android/iOS/backend CICD build statuses and test report outcomes. Single-handedly
Employed
・
Open to opportunities
Full-time / Interested in working remotely
More than 15 years
UNSW
・
Computer software, network and security engineeringPast
iOS Engoneer @VoiceTube 紅點子科技股份有限公司2020 ~ 2023
・
iOS Developer
Within three months
Unemployed
・
Open to opportunities
Full-time / Interested in working remotely
6-10 years
國立臺北大學 National Taipei University
・
資工所Data Engineer @MediaTek Inc.2019 ~ Present
・
Senior Software Engineer
Within one month
Employed
・
Open to opportunities
Full-time / Interested in working remotely
6-10 years
National Tsing Hua University
・
Data Mining, Database System
Engineering Manager @Viscovery 創意引晴股份有限公司2018 ~ Present
・
Within one month
including Recurrent Neural Network (RNN), word2vec, paragraph vector. Working Experience Intern, JulDec 2014 Phalanx Biotech Develop machine learning solutions for cancer gene detection and TSMC semiconductor quality prediction. Intern, JulAug 2013 Solid State System Assisted in CMOS MEMs microphone product development including proto-type simulation, fabrication/packaging process, defect analysis and market survey. Achievements 1. Qualified of preliminary contest of TSMC Big Data Analytics for Semiconductor Manufacturing. Taiwan,Completion of Broad-DREAM Gene Essentiality Prediction Challenge, 2014 Professional Skills Research Field Machine Learning Deep Learning Computer Vision Natural Language Processing Lang...
Employed
・
Open to opportunities
Full-time / Interested in working remotely
4-6 years
國立台灣大學
・
Electrical Engineering
Blockchain Enginner & AI Lead @Portal Network
Blockchain engineer & Blockchain consulting
Within one month
expressions, enhancing user interaction experiences. Emotion Recognition Systems: Developing AI systems capable of understanding and responding to user emotions, used to enhance customer service and user interactions. Voice Technology (TTS): Natural Voice Generation: Employing Text-to-Speech (TTS) technology to convert text into natural, fluent voice across multiple languages, suitable for virtual assistants and customer service bots. Voice Interaction Interfaces: Developing voice interaction interfaces that enable digital personas to conduct natural dialogues, offering more humanized interaction methods. Large Language Models and Natural Language Processing: Personalized Response Generation: Leveraging personality-driven large language models to provide customized
Full-time / Interested in working remotely
4-6 years
Senior Product Growth Manager @SOSV2018 ~ 2023
・
Product Manager / Product Growth
Within one month
and honed my skills in dynamic tech stacks. As a seasoned product manager, I've successfully navigated the intersection of user needs and technical innovation, steering product strategies for optimal growth. From leading software teams at IBM to co-founding a startup that delved into machine learning and natural language processing, I bring a unique blend of technical prowess, product expertise, and entrepreneurial flair. My expertise in growth hacking working with early stage startups around the world involves leveraging lean startup methodologies and data-driven decision-making to drive business success. I'm excited to bring
Employed
・
Not open to opportunities
Full-time / Interested in working remotely
10-15 years
Quantic School of Business and Technology
Active
Active
人工智慧工程師 @玉山銀行智能金融處 (Intelligent Finance Dept., E-SUN Bank )2020 ~ Present
・
Machine Learning Researcher / Engineer
Within one month
Jeffrey Lin ( 林奕勳 ) ESUN Bank, Intelligent Finance Technology Department, AI Engineer focusing on Enterprise-Level ML Frameworks and Design Patterns [email protected] TW, Taipei CAREER SUMMARY 6+ years of deep learning research and development experience, with a strong ability in prototyping, evaluation, and deployment of models, in both academical and industrial environment. 2+ years of financial AI development experience, specialized in the design of python API, SDK, and framework for optimizing the deployment flow of data science, ETL, and machine learning pipelines. 1+ year of participation in the technology management
Employed
Full-time / Interested in working remotely
4-6 years
National Taiwan University
・
Graduate Institute of Communication EngineeringThe Most Lightweight and Effective Recruiting Plan
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Within one month
AI Engineer
玉山銀行智能金融處 (Intelligent Finance Dept., E-SUN Bank )
・
2020 ~ Present
Professional Background
Current status
Employed
Job Search Progress
Professions
Data Engineer, Machine Learning Engineer
Fields of Employment
Software, Banking
Work experience
1-2 years work experience (4-6 years relevant)
Management
I've had experience in managing 1-5 people
Skills
Languages
English
・
Intermediate
Job search preferences
Positions
Machine Learning Researcher / Engineer
Job types
Full-time
Locations
台灣台北市, 台灣新竹市
Remote
Interested in working remotely
Freelance
Yes, I freelance in my spare time
Educations
School
National Taiwan University
Major
Graduate Institute of Communication Engineering
CAREER SUMMARY
- 6+ years of deep learning research and development experience, with a strong ability in prototyping, evaluation, and deployment of models, in both academical and industrial environment.
- 2+ years of financial AI development experience, specialized in the design of python API, SDK, and framework for optimizing the deployment flow of data science, ETL, and machine learning pipelines.
- 1+ year of participation in the technology management assistant bootcamp, with real-experience on business user support, gaining first-hand insight on financial and corporative AI usage via cross-department collaboration.
- Having excellent code-quality standard, efficiency-oriented thinking, and perseverance in face of challenging problems; Continue absorbing “technical-nutrients” from the development of open-source AI community.
PROJECT
Next-Generation ETL Framework for Data Science Team
2022/7-2022/12
- Goal:
- Design a lightweight ETL python framework enabling data scientists to seamlessly deploy their pandas/SQL processes to airflow in a maintainable format.
- Reduce the maintenance and optimization cost of AI projects with integration of customizable data validation, metadata registration, and time/memory-profiling tools.
- Ensure flexibility and extensibility in pandas-like packages (such as CuDF, Polars, PySpark, etc) for future parallelizability, targeting 1000X speedup.
- Responsibility: Coordinate the design and development of ETL framework components. Give instruction to ETL research intern and provide code review and design guidance for the project participants.
- Accomplishment: Have wrapped the framework into a workable python package and successfully imported it into two production projects, with ETL business logic automatically rendered on the Airflow UI in an easily traceable way.
Enhance and Maintain House Price and Mortgage Loan API
2022/2-2022/7
- Responsibility: API Service maintenance, code refactoring, and model performance enhancement for the house-price/mortgage loan automatic appraisal system.
- Accomplishment:
- Encrypted API re-routing:
- Re-route the internal mortgage API service as an encrypted API service for external client. Understand and implement the RSA/AES double encryption protocol under spec-ambiguous situation by investigating client-side PHP code.
- Code refactor and migration:
- Reduce the cognitive complexity of service code from 1000-line-single-file situation by thoroughly understand the business logic and refactoring it into chain-of-command design pattern.
- Design common module with logging mechanism to reduce future maintenance cost.
- Draw and carry out the code migration plan without compromising the 24/7-criteria.
- Re-design of house appraisal model for interpretability:
- Coordinate with research institution and business unit to build a business interpretable deep learning model.
- Re-design the loss function with regularization term to meet the legal requirements.
- Reducing the model inference time by adopting Google’s state-of-the-art nearest neighbor algorithm for referential house searching.
Scalable ETL Pipeline for Credit Loan Marketing Data
2021/8-2022/1
- Responsibility:
- Orchestrate the cross-department coordination of the development process.
- Design a low-code python-SQL interface for the data-analyst of business unit and provide development and CICD guidance.
- Ensure business-logic-extensibility in considerations of future marketing campaign.
- Ensure timely delivery of 7 million customer campaign data under 1day computation criteria.
- Accomplishment:
- Have helped the data-analyst of business unit successfully incorporate new campaign business logic into the production pipeline.
- A comprehensive tutorial to the development interface and CICD toolkits, enabling smooth new feature extension with zero-guidance.
- Significantly speedup the pipeline from 5d to 8h via map-reduce parallelization with multiple Airflow worker nodes.
PyTorch development framework for Industry-Academy Cooperation
2021/4-2022/8
- Goal: Develop Pytorch model development framework to reduce the academy-to-company code migration cost.
- Features:
- Support training, validation, testing, weight/performance inspection, and check-pointing of models with different architecture.
- Fixed experimentation flow for all models with formatted running script and folder structure, to enhance code readability and reproducibility.
- Support lazy evaluation and visualization of data pre-processing, avoid re-generation of data in experimentation scenario.
- Supporting parallel model hyper-parameter tuning via Ray (a general python parallelization framework).
- Tools: Pytorch-lightning, Ray, TensorBoard, pyflow-viz
2D Indoor Positioning System through Android App
2014/1-2015/1
Instructor: Professor Ren-Song Tsay
- Survey on the state-of-the-art indoor positioning technologies and write a project proposal. Investigate the relationship between distance and the amplitude of the Bluetooth signal through experiment. Develop an android app that fuses the signals of Bluetooth and Geo sensor for position estimation.
- Score: A+
RESEARCH
Develop techniques applicable to the music industry and design experiments for proof-of-concept
2015/9-2020/1
Instructor: Professor Homer H. Chen
- Developed a context-based tag propagation method to reduce the training noise and successfully improved a deep music auto-tagging model (SampleCNN) by 25.8% in MAP.
- Enhance the robustness of the tag propagation method and successfully extended the improvement to two other neural networks (CRNN and 2D-CNN) with the increase of improvement reaches 6.5%, 4.9%, and 5.5% (for SampleCNN, CRNN, and 2D-CNN, respectively).
- Responsibility: Build up the experiment acceleration (via GPU) environment on a Linux-based server; Design the model evaluation and training strategy with TensorFlow.
- Conduct error analysis to understand the problem in the training data using Matplotlib and Seaborn. Tune the hyper-parameters during model development. Accelerate the evaluation and data visualization process using ray, a parallel processing package.
Research proposal in music for lab funding from the Ministry of Science and Technology (MOST)
2015/9-2017/10
- Wrote research plans about how lyrics may help to improve the user experience of a music recommendation system.
- Research proposals: 1) Interactive, Context-Aware Music Recommendation and 2) A Unified Framework for Processing and Understanding Heterogeneous Data for Intelligent Recommendation.
EDUCATION
M.S. in Graduate Institute of Communication Engineering, National Taiwan University
2015/9 ~ 2020/1
- Multimedia Signal Processing (A)
- Special Topics on Internet of Things (A): Develop an IOT product and demonstrate its functionality.
- Computational Methods and Tools for Data Science (A): Familiarize myself with basic EDA (exploratory data analysis) techniques including PCA and t-SNE.
B.S. in Electrical Engineering Department, National Tsing Hua University
2011/9 ~ 2015/6
- Programming Classes: Logic Design (A+) / Logic Design Lab (A+) / Embedding System Lab (A+) / Computer Programming Design (A+)
- Math Classes: Calculus I&II (A+/A+) Linear Algebra (A+) / Numerical Analysis (A)
SKILLS
- Programming Skills: Design Patterns、ETL、Image Building、Machine Learning、Parallel Acceleration、Python SDK Design、Refactoring
- Development Environment: Linux, Docker, Oracle-Cloud, Visual Studio Code
- Computer Languages: Python、C++、SQL、JAVA
- Tools for Machine Learning and Data Science: Pytorch、Pytorch-Lightning、CuDF (GPU-accelerating dataframe)、Pandas、Networkx、LightGBM、DeepGraphLibrary
- Tools for Model Serving, Deployment, and Data Pipeline: Airflow、FastAPI、 k8S、PostgresDB、RedisGraph、Neo4j、Ray
For Collaboration: Github、Azure DevOps
CAREER SUMMARY
- 6+ years of deep learning research and development experience, with a strong ability in prototyping, evaluation, and deployment of models, in both academical and industrial environment.
- 2+ years of financial AI development experience, specialized in the design of python API, SDK, and framework for optimizing the deployment flow of data science, ETL, and machine learning pipelines.
- 1+ year of participation in the technology management assistant bootcamp, with real-experience on business user support, gaining first-hand insight on financial and corporative AI usage via cross-department collaboration.
- Having excellent code-quality standard, efficiency-oriented thinking, and perseverance in face of challenging problems; Continue absorbing “technical-nutrients” from the development of open-source AI community.
PROJECT
Next-Generation ETL Framework for Data Science Team
2022/7-2022/12
- Goal:
- Design a lightweight ETL python framework enabling data scientists to seamlessly deploy their pandas/SQL processes to airflow in a maintainable format.
- Reduce the maintenance and optimization cost of AI projects with integration of customizable data validation, metadata registration, and time/memory-profiling tools.
- Ensure flexibility and extensibility in pandas-like packages (such as CuDF, Polars, PySpark, etc) for future parallelizability, targeting 1000X speedup.
- Responsibility: Coordinate the design and development of ETL framework components. Give instruction to ETL research intern and provide code review and design guidance for the project participants.
- Accomplishment: Have wrapped the framework into a workable python package and successfully imported it into two production projects, with ETL business logic automatically rendered on the Airflow UI in an easily traceable way.
Enhance and Maintain House Price and Mortgage Loan API
2022/2-2022/7
- Responsibility: API Service maintenance, code refactoring, and model performance enhancement for the house-price/mortgage loan automatic appraisal system.
- Accomplishment:
- Encrypted API re-routing:
- Re-route the internal mortgage API service as an encrypted API service for external client. Understand and implement the RSA/AES double encryption protocol under spec-ambiguous situation by investigating client-side PHP code.
- Code refactor and migration:
- Reduce the cognitive complexity of service code from 1000-line-single-file situation by thoroughly understand the business logic and refactoring it into chain-of-command design pattern.
- Design common module with logging mechanism to reduce future maintenance cost.
- Draw and carry out the code migration plan without compromising the 24/7-criteria.
- Re-design of house appraisal model for interpretability:
- Coordinate with research institution and business unit to build a business interpretable deep learning model.
- Re-design the loss function with regularization term to meet the legal requirements.
- Reducing the model inference time by adopting Google’s state-of-the-art nearest neighbor algorithm for referential house searching.
Scalable ETL Pipeline for Credit Loan Marketing Data
2021/8-2022/1
- Responsibility:
- Orchestrate the cross-department coordination of the development process.
- Design a low-code python-SQL interface for the data-analyst of business unit and provide development and CICD guidance.
- Ensure business-logic-extensibility in considerations of future marketing campaign.
- Ensure timely delivery of 7 million customer campaign data under 1day computation criteria.
- Accomplishment:
- Have helped the data-analyst of business unit successfully incorporate new campaign business logic into the production pipeline.
- A comprehensive tutorial to the development interface and CICD toolkits, enabling smooth new feature extension with zero-guidance.
- Significantly speedup the pipeline from 5d to 8h via map-reduce parallelization with multiple Airflow worker nodes.
PyTorch development framework for Industry-Academy Cooperation
2021/4-2022/8
- Goal: Develop Pytorch model development framework to reduce the academy-to-company code migration cost.
- Features:
- Support training, validation, testing, weight/performance inspection, and check-pointing of models with different architecture.
- Fixed experimentation flow for all models with formatted running script and folder structure, to enhance code readability and reproducibility.
- Support lazy evaluation and visualization of data pre-processing, avoid re-generation of data in experimentation scenario.
- Supporting parallel model hyper-parameter tuning via Ray (a general python parallelization framework).
- Tools: Pytorch-lightning, Ray, TensorBoard, pyflow-viz
2D Indoor Positioning System through Android App
2014/1-2015/1
Instructor: Professor Ren-Song Tsay
- Survey on the state-of-the-art indoor positioning technologies and write a project proposal. Investigate the relationship between distance and the amplitude of the Bluetooth signal through experiment. Develop an android app that fuses the signals of Bluetooth and Geo sensor for position estimation.
- Score: A+
RESEARCH
Develop techniques applicable to the music industry and design experiments for proof-of-concept
2015/9-2020/1
Instructor: Professor Homer H. Chen
- Developed a context-based tag propagation method to reduce the training noise and successfully improved a deep music auto-tagging model (SampleCNN) by 25.8% in MAP.
- Enhance the robustness of the tag propagation method and successfully extended the improvement to two other neural networks (CRNN and 2D-CNN) with the increase of improvement reaches 6.5%, 4.9%, and 5.5% (for SampleCNN, CRNN, and 2D-CNN, respectively).
- Responsibility: Build up the experiment acceleration (via GPU) environment on a Linux-based server; Design the model evaluation and training strategy with TensorFlow.
- Conduct error analysis to understand the problem in the training data using Matplotlib and Seaborn. Tune the hyper-parameters during model development. Accelerate the evaluation and data visualization process using ray, a parallel processing package.
Research proposal in music for lab funding from the Ministry of Science and Technology (MOST)
2015/9-2017/10
- Wrote research plans about how lyrics may help to improve the user experience of a music recommendation system.
- Research proposals: 1) Interactive, Context-Aware Music Recommendation and 2) A Unified Framework for Processing and Understanding Heterogeneous Data for Intelligent Recommendation.
EDUCATION
M.S. in Graduate Institute of Communication Engineering, National Taiwan University
2015/9 ~ 2020/1
- Multimedia Signal Processing (A)
- Special Topics on Internet of Things (A): Develop an IOT product and demonstrate its functionality.
- Computational Methods and Tools for Data Science (A): Familiarize myself with basic EDA (exploratory data analysis) techniques including PCA and t-SNE.
B.S. in Electrical Engineering Department, National Tsing Hua University
2011/9 ~ 2015/6
- Programming Classes: Logic Design (A+) / Logic Design Lab (A+) / Embedding System Lab (A+) / Computer Programming Design (A+)
- Math Classes: Calculus I&II (A+/A+) Linear Algebra (A+) / Numerical Analysis (A)
SKILLS
- Programming Skills: Design Patterns、ETL、Image Building、Machine Learning、Parallel Acceleration、Python SDK Design、Refactoring
- Development Environment: Linux, Docker, Oracle-Cloud, Visual Studio Code
- Computer Languages: Python、C++、SQL、JAVA
- Tools for Machine Learning and Data Science: Pytorch、Pytorch-Lightning、CuDF (GPU-accelerating dataframe)、Pandas、Networkx、LightGBM、DeepGraphLibrary
- Tools for Model Serving, Deployment, and Data Pipeline: Airflow、FastAPI、 k8S、PostgresDB、RedisGraph、Neo4j、Ray
For Collaboration: Github、Azure DevOps