機器學習工程師

職務說明：
1. 負責協助公司內部產品開發流程的智慧化與自動化
2. 提升 Digital Design Verification (DV) 的驗證效率與品質
3. 工作常與不同部門協調和討論需求，重視團隊溝通與合作，互相支援提醒

職務內容：
1. 將機器學習與人工智慧導入現有的工作流程，並與自動化系統整合
2. 與前端、後端工程師及數位設計驗證 (DV) 工程師合作，開發客製化的 data-driven 應用程式
3. 建構 Data 與 ML Pipeline
4. 開發與維護 ML Model、AI 上線後的性能監測與再訓練
5. 開發與維護 數據清洗、數據串接、數據監控、數據品質管理、Dashboard
6. 設計實驗以評估與分析導入 AI 後帶來的效益

職缺需求：
1. 精通 Python 3
2. 具備 Machine Learning 觀念、任一 ML / DL Framework
3. 具備 LLM 觀念與應用開發經驗
4. 具自行閱讀 AI / ML 論文的能力
5. 具資料科學相關背景，熟悉資料分析、資料視覺化、資料清理、資料處理
6. 具 Time Series Analysis and Forecasting、NLP、DRL、GNN 經驗者尤佳
7. 具 LangChain、LlamaIndex、vLLM、LLaMA Factory 等 LLM Inference 框架與 LLM Fine-tuning 使用經驗者尤佳
8. 具 RAG、Vector Database 開發經驗者尤佳
9. 具 AI Agentic Workflow 開發經驗者尤佳
10. 具 MLOps、MLflow 經驗者尤佳
11. 具 SystemVerilog、SVA、UVM、Design Verification 經驗者尤佳
12. 具問題發現能力、問題分析能力、問題組織能力、問題解決能力、問題追蹤能力、溝通能力
13. Data Science、Data Engineering、Computer Science、Information Engineering、Electrical and Electronic Engineering、Physics 等相關科系畢業者佳

Position Overview:
1. Support the intelligentization and automation of the internal product development processes
2. Enhance the efficiency and quality of Digital Design Verification (DV)
3. Collaborate across departments to address requirements, with emphasis on team communication, cooperation, and mutual support

Key Responsibilities:
1. Implement machine learning and artificial intelligence solutions into existing workflows and integrate with automated systems
2. Collaborate with frontend, backend, and digital design verification (DV) engineers to develop customized data-driven applications
3. Construct Data and ML pipelines
4. Develop and maintain ML models, including post-deployment performance monitoring and retraining
5. Develop and maintain systems for data cleaning, integration, monitoring, quality management, and dashboards
6. Design experiments to evaluate and analyze the benefits of AI implementation

Qualifications:
1. Expert proficiency in Python 3
2. Strong understanding of Machine Learning concepts and experience with at least one ML/DL framework
3. Knowledge of Large Language Models (LLMs) and experience in LLM application development
4. Ability to independently read and comprehend AI/ML research papers
5. Data science background with expertise in data analysis, visualization, cleaning, and processing
6. Strong problem identification, analysis, organization, solving, and tracking abilities, plus excellent communication skills

Preferred Qualifications:
1. Experience with Time Series Analysis and Forecasting, Natural Language Processing (NLP), Deep Reinforcement Learning (DRL), or Graph Neural Networks (GNN)
2. Experience with LLM inference frameworks such as LangChain, LlamaIndex, vLLM, and LLM fine-tuning framework such as LLaMA Factory.
3. Experience with Retrieval-Augmented Generation (RAG) and Vector Database development
4. Experience developing AI Agentic Workflows
5. Experience with MLOps and MLflow
6. Experience with SystemVerilog, SVA, UVM, or Digital Design Verification (DV)

Academic Background:
Data Science, Data Engineering, Computer Science, Information Engineering, Electrical and Electronic Engineering, or Physics-related fields.