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NVIDIA’s Solution Architecture team is looking for a Data Science focused Solution Architect with expertise in Machine Learning (ML), Deep Learning (DL) and Data Science platforms. We work with the most exciting computing hardware and software, driving the latest breakthroughs in artificial intelligence. We need individuals who can enable customer productivity and develop lasting relationships with our technology partners, making NVIDIA an integral part of end-user solutions. We are looking for

Role: AI Chatbot Engineer Job Summary: As a Chatbot Engineer, you will be responsible for developing and implementing chatbot applications for our clients. You will work closely with the development team to design, build, and maintain chatbots that meet the client's requirements. You will also ensure that the chatbot applications are scalable, reliable, and secure.

作為廣告引擎的資深機器學習工程師，您的主要職責是利用先進的機器學習技術來提升廣告系統的效能和智能化。設計、開發和實施機器學習模型，以提高廣告的目標達成率、點擊率和轉化率。將

【熱血團隊】 域動 Data Team 是由一群對『數據分析』及『AI 應用』具有研究及開發熱誠的成員所組成。在這裡你可以接觸到大量即時的使用者行為資料，並將資料科學模型應用於: 『網路廣告投放優化』 『網路用

【 Our Vision 】 Because of the shortage of radiologists in worldwide, there are many people who lose their lives. We want to provide quality medical care to them. By using AI, we are providing a way to diagnose diseases rapidly on-site and in early stage! Our company are providing medical equipment with embedded AIs in worldwide such as a mobile X-ray machine! Right now, we are saving many people’s lives using our AI-embedded medical

1、開發經驗數據分析，特別是LLM和多模式基礎模型。 2、使用深度學習框架，如TensorFlow和PyTorch的經驗。 3、使用RAG，SelfRAG，VectorDb，GraphDB的經驗。 4、使用LLM優化框架的經驗，例如VLLM、 Ex-llama、 llama.cpp。 5、使用 Docker、Kubernetes 、Triton Inference Server 或 TensorRT的經驗。 6

世界上沒有一個模型不能解決的問題。 如果有，那就再做一個。 Authme 致力於提供最快速、安全與便利的數位身分驗證服務，身為Authme的電腦視覺工程師，您會和工程團隊的App工程師、前後端工程師們一起

主要職責: 智慧物流解決方案用AMR(Autonomous Mobile Robot) /AGV( Automated Guided Vehicle)自主導航相關演算法開發與維護 主要任務: A.開發智慧物流解決方案用AMR/AGV自主導航相關演算法 a.物件辨識(Object Recognition) b.同時定位與

負責人工智慧學習演算法開發包含： 1. 規劃演算法架構 2. 演算法實作及驗證 3. 進行演算法精確度及效能分析 4. 疊代式調整演算法以達成客戶需求

主要負責內容為： 1. 建構各種Data Collection Pipelines 2. 建立自動及半自動化的Model Training Pipelines 3. 使用既有或引進新的工具來管理Datasets/Models/Configs 4. 部署ML Applications 5. 監控ML 服務以及協助除錯