機器學習工程師

職務說明：

• 負責協助公司內部產品開發流程的智慧化與自動化
• 提升 Digital Design Verification (DV) 的驗證效率與品質
• 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：

• 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
• 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
• 建構 Data 與 ML Pipeline
• 開發與維護 ML Model、AI 上線後的性能監測與再訓練
• 開發與維護 數據清洗、數據串接、數據監控、數據品質管理、Dashboard
• 設計實驗以評估與分析導入 AI 後帶來的效益

職缺需求：

• 精通 Python 3
• 具備 Machine Learning 觀念、任一 ML / DL Framework
• 具備 LLM 觀念與應用開發經驗
• 具自行閱讀 AI / ML 論文的能力
• 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
• 具 Time Series Analysis and Forecasting、NLP、DRL、GNN 經驗者尤佳
• 具 LangChain、LlamaIndex、vLLM、LLaMA Factory 等 LLM Inference 框架與 LLM Fine-tuning 使用經驗者尤佳
• 具 RAG、Vector Database 開發經驗者尤佳
• 具 AI Agentic Workflow 開發經驗者尤佳
• 具 MLOps、MLflow 經驗者尤佳
• 具 SystemVerilog、SVA、UVM、Design Verification 經驗者尤佳
• 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
• Data Science、Data Engineering、Computer Science、Information Engineering、Electrical and Electronic Engineering、Physics 等相關科系畢業者佳

Position Overview:

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

Key Responsibilities:

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

Qualifications:

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

Preferred Qualifications:

• Experience with Time Series Analysis and Forecasting, Natural Language Processing (NLP), Deep Reinforcement Learning (DRL), or Graph Neural Networks (GNN)
• Experience with LLM inference frameworks such as LangChain, LlamaIndex, vLLM, and LLM fine-tuning framework such as LLaMA Factory.
• Experience with Retrieval-Augmented Generation (RAG) and Vector Database development
• Experience developing AI Agentic Workflows
• Experience with MLOps and MLflow
• 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.