Cake 找工作

Role OverviewWe are seeking an experienced Machine Learning Researcher to join our research team. This role requires expertise in designing and deploying deep learning models within high-performance, low-latency trading systems. You will be working on developing robust, scalable models and integrating them into our trading infrastructure.Responsibilities Data Analysis Preprocessing: Understand and preprocess orderbook data.Deep Learning Model Design: Design models for time-series and orderbook data (Transformers, RNNs, CNNs, Attention).Scalable Training Implementation: Implement parallelized data loading pipelines.Feature Engineering: Develop and optimize orderbook features using C++.Backtesting Evaluation: Conduct rigorous backtesting across markets.Production Integration: Deploy models into real-time, low-latency systems.

【公司介紹】 這是一家全球型科技服務公司，長期協助大型企業推動數位轉型與智慧化升級，在金融、醫療、製造與零售等產業皆有長期合作客戶。團隊遍佈全球多個科技據點，並與國際級企業合作開發下一代 AI 與資料應用解決方案。 目前台灣團隊正參與多項 AI 應用與智慧化平台專案，包含企業 AI 助手、智慧客服、AI Agent 與生成式 AI 應用。工程師能直接參與 AI 系統從設計、開發到部署的完整流程，並與海外技術團隊合作，接觸大型企業級 AI 專案與跨國技術交流。 如果你希望將 AI 技術真正落地到企業應用場景，並在全球化環境中持續提升技術深度與實務經驗，這將是一個能快速累積 AI 專案經驗的機會。 【工作內容】 參與 AI 相關應用系統與工具的設計與開發開發與整合 AI 應用，例如 AI Chatbot、AI Agents 等參與 AI 模型開發、部署與應用整合與跨國團隊合作，協助企業導入 AI 解決方案參與系統規劃、技術架構討論與產品優化 【使用技術】 PythonJavaScriptC# / C++Machine Learning / Generative AIBig Data AnalysisAI Chatbot / AI AgentsWindows UWP / Win32 Service（加分）

公司介紹 這是一家長期深耕金融科技與數據應用的知名金融機構，擁有龐大的金融交易與客戶數據基礎，持續投入 AI、數據分析與智能化服務發展。團隊致力於將機器學習技術實際應用在金融服務場景中，例如智能推薦、風險分析、語言模型應用與流程自動化等，讓 AI 技術真正影響數百萬用戶的金融體驗。 在這個團隊中，你將有機會參與大型數據環境下的 AI 專案，將模型從研究、開發到實際部署，並與產品與業務團隊合作，將機器學習能力落地到實際商業場景。對於希望將 AI 技術真正應用於高影響力產業場景 的工程師而言，是非常具有發展性的舞台。 工作內容 開發與優化機器學習模型，應用於金融服務與內部流程優化 與業務單位合作，將實際業務問題轉換為可落地的機器學習問題 建置與部署 AI 模型服務，提升系統自動化與智能化程度 參與模型設計、實驗、評估與部署流程 協助推動 AI 技術在產品與服務中的應用 使用的技術 Python TensorFlow / PyTorch / Scikit-learn RESTful API NLP / 語音模型 / 推薦系統 / 影像辨識（依專案需求）

公司介紹 一家深耕軟體研發多年的國際化技術團隊，專注於打造高效能資料系統與即時交易相關平台，產品與服務橫跨多個海外市場。團隊致力於將資料工程與機器學習技術落地於實際商業場景，透過大規模資料分析與智慧化模型，提升系統效率與風險識別能力。 公司工程文化重視技術討論與跨團隊合作，工程師不僅負責模型開發，也能參與資料架構、系統設計與模型部署，直接看到技術對產品與業務帶來的實際影響。此外，公司採 Hybrid 工作模式，並提供完善的辦公環境與員工福利，打造兼具效率與生活品質的研發氛圍。 工作內容 從大型資料庫中進行資料擷取、清理與分析，建立可用於模型訓練與分析的資料基礎 設計並開發基於 資料探勘與機器學習技術 的內部自動化系統與工具 協助將機器學習模型部署至實際產品或系統環境，並確保系統穩定運行 與工程、產品與資料相關團隊合作，提供 ML 技術建議並評估模型應用場景 持續優化資料處理流程與模型效能，提升系統整體效率 技術環境 Programming / ML：Python、PyTorch、TensorFlow、scikit-learn Data / Database：SQL、大型資料庫資料處理 Infrastructure：Linux、Shell Script、Docker / Container Machine Learning：Regression、Neural Network、Decision Tree、Clustering、Dimensionality Reduction

We are looking for a savvy ML Engineer intern. The hire will join the development and optimization of our ML applications. If you want to join a world-class data team, we look forward to hearing from you soon!

我們正在尋找積極、熱愛挑戰的 AI 開發實習生加入 Tera Thinker 的團隊，參與 AI 及 LLM 系統的開發。 在實習期間，你將直接投入實際的產品開發與運作。除了開發並維護目前已上線且服務全台數百所高中的線上學習平台之外，你也將從零開始參與全新服務的開發，親身體驗產品從構想到正式營運的完整流程。透過這份實習，你不僅能累積豐富的軟體工程實務經驗，更能在快速變化的 AI 時代中，培養出人機協作的產品發展與開發思維。 工作內容 技術研究（20%）針對最新的 AI 技術進行調查，研讀論文、程式碼與相關資料實際測試最新的技術或解決方案，並與現行或替代方案進行應用的比較與評估，最後形成決策建議現有服務的改進與重構（40%）分析服務數據，尋找潛在問題，觀察成因，設計改善實驗與解決方案實作方案、進行測試，在不影響既有服務的狀況下部署方案，分析成效並持續改善新服務的設計與開發（40%）了解最新的技術、產品發展及應用現況與 PO 緊密合作，設計真正符合市場需求的功能應用及實現方案快速的從頭打造 Prototype 及 MVP（Minimum Viable Product 最小可行性產品）

Our Product Vulcan is a cybersecurity solution specifically designed for GenAI, offering two core services: Red Team (vulnerability assessment) and Blue Team (real-time defense). It ensures GenAI compliance, cybersecurity robustness, and operational integrity. Since its official launch in 2024, Vulcan has been recognized by the international standard-setting organization OWASP as a certified vendor for LLM GenAI security testing and assessment. It is one of the few solutions capable of supporting multiple Asian languages (Traditional Chinese, Simplified Chinese, Japanese, Korean, Thai) and Standard Arabic. Learn more about us 👉 Vulcan product: https://vulcanlab.ai/Vulcan LinkedIn: https://www.linkedin.com/company/vulcanlab-ai/AIFT group: https://aift.io/Tech Blog: https://medium.com/onedegree-tech-blog About the role We are looking for a talented Machine Learning Engineer to join our Product Core Engineering team. You will be responsible for building and optimizing machine learning workflows that directly power our AI-driven products. This role focuses on the full lifecycle of model development — from training and fine-tuning to deployment and monitoring — ensuring robust and efficient ML systems at scale. Why Join Us? Product Impact: Your work will be directly embedded in our core AI products, shaping user experience and product capabilities.Engineering Excellence: Be part of a team that values high-quality engineering, reproducibility, and scalability.Innovation: Opportunity to experiment with cutting-edge ML and GenAI technologies in production settings.Collaboration: Work alongside backend, platform, and product teams in a highly collaborative environment.Competitive Package: Receive attractive compensation and benefits aligned with your skills and performance.How to Apply Please apply this position through 👉 https://job-boards.greenhouse.io/aift/jobs/5652688004 It will help us process your applications faster!! Key Responsibilities Model Development: Design and implement training processes for machine learning classifiers and generative models.Fine-tuning Prompting: Adapt pre-trained models to specific product needs through fine-tuning, prompt engineering, and parameter optimization.Hyperparameter Management: Configure and tune hyperparameters to balance accuracy, robustness, and performance.Pipeline Engineering: Build scalable training and evaluation pipelines to support continuous experimentation.Integration: Collaborate with backend and product engineers to deploy models into production systems.Monitoring Maintenance: Establish monitoring metrics and retraining strategies to maintain model performance in dynamic environments. -

Our Product Vulcan is a cybersecurity solution specifically designed for GenAI, offering two core services: Red Team (vulnerability assessment) and Blue Team (real-time defense). It ensures GenAI compliance, cybersecurity robustness, and operational integrity. Since its official launch in 2024, Vulcan has been recognized by the international standard-setting organization OWASP as a certified vendor for LLM GenAI security testing and assessment. It is one of the few solutions capable of supporting multiple Asian languages (Traditional Chinese, Simplified Chinese, Japanese, Korean, Thai) and Standard Arabic. Learn more about us 👉 Vulcan product: https://vulcanlab.ai/Vulcan LinkedIn: https://www.linkedin.com/company/vulcanlab-ai/AIFT group: https://aift.io/ About the role We are seeking an experienced Machine Learning Lead to helm our Machine Learning team. In this pivotal role, you will be the engineering architect behind Vulcan’s core AI capabilities. You will act as the nexus between Research, Platform, and Product. Your mission is to translate cutting-edge findings on GenAI threats into robust, production-ready machine learning models that power our GenAI Security Guardrails (Blue Team) and Automated Vulnerability Assessment (Red Team). Crucially, you will serve as the bridge between deep tech and business strategy, articulating technical constraints (like FLOPS and latency) to leadership and clients while guiding the engineering direction.How to Apply Please apply this position through 👉 https://job-boards.greenhouse.io/aift/jobs/5797377004 It will help us process your applications faster!! Key Responsibilities 1. Model Development Optimization (Training Fine-tuning): Research to Production: Collaborate with the Security Research Team to operationalize new threat detection techniques. They identify the "what" (e.g., new prompt injection patterns); you determine the "how" (model architecture, training strategy). Fine-tuning Adaptation: Lead the fine-tuning of Language Models (e.g., using LoRA/PEFT) to optimize for our supported muti-lingual languages and specific security intents. Multimodal Readiness: Prepare the system for Multimodal (Text + Image/Audio) capabilities. Evaluate and implement models to detect visual prompt injections and non-textual threats as the product evolves. 2. MLOps Data Infrastructure: Enhance Scale MLOps: Take ownership of our existing ML pipelines. Focus on optimizing and scaling CI/CD/CT workflows to improve training efficiency and deployment velocity. Data Governance: Implement and enforce rigorous Data Versioning strategies (e.g., DVC) to ensure complete reproducibility of model artifacts and datasets. Monitoring Reliability: Maintain rigorous monitoring for model drift and performance, ensuring high reliability in a production security environment. 3. Cross-Functional Implementation Leadership: Platform Collaboration: Work closely with the Platform Engineering Team to integrate ML models into the broader product architecture. Ensure seamless interaction between model inference services and the main platform logic. Team Leadership: Lead and mentor Machine Learning Engineers, fostering a culture of engineering rigor, code quality, and operational excellence. Resource Management: Manage GPU resources and compute budgets effectively for both training and inference workloads. 4. Technical Strategy Stakeholder Management: Translating Tech to Business: Act as the technical voice of the ML team. You must effectively explain complex ML concepts (e.g., FLOPS, quantization trade-offs, model latency vs. accuracy) to executive leadership and clients. Cost-Benefit Analysis: Justify compute resource investments. Articulate the trade-off between infrastructure costs (GPU hours) and performance gains to non-technical stakeholders. -

Established in 1987 and headquartered in Taiwan, TSMC pioneered the pure-play foundry business model with an exclusive focus on manufacturing its customers’ products. As of 2024, TSMC serves more than 500 customers and manufactures over 11,000 products for high-performance computing, smartphones, the Internet of Things (IoT), automotive, and digital consumer electronics. It is the world’s largest provider of logic ICs, with an annual capacity of 16 million 12-inch equivalent wafers. TSMC operates fabs in Taiwan as well as manufacturing subsidiaries in Washington State, Japan and China, and the Company began construction on a specialty technology fab in Dresden, Germany, in 2024. In Arizona, TSMC is building three fabs, with the first starting 4nm production in 2025, the second by 2028, and the third by the end of the decade.Responsibilities: AI algorithm/application research and development on image recognition, time series forecasting, and abnormal detectionAI system design and development, including system infrastructure (docker/K8S) and web application.AI System analysis, design, development and integration for cross organizations/systems projects.Innovate, develop and introduce Intelligent Manufacturing Solutions.Hiring Organization: IMC

・與產品、風控、運營團隊協作，針對業務需求定義資料模型與監控指標・建置 ETL 流程，收集與清洗來自平台的行為數據（如下注記錄、轉碼、點擊行為）・開發與維護異常偵測模型（如洗碼對打、機器人行為、套利用戶）・利用機器學習或統計模型預測玩家留存、LTV、流失風險・設計風控策略，提升平台資金與行爲風險控制能力・定期產出分析報告，提出可行的產品或營運優化建議