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【職務核心】 您將利用海量數據構建核心決策模型，透過自動化手段解決內部的營運痛點。我们需要您具備將數學理論轉化為生產級 (Production-grade) 程式碼的能力。 數據煉金：使用 SQL 從龐大的資料倉儲中萃取 (Extraction) 有價值的特徵，並進行深度分析。模型開發與自動化：應用 Data Mining 與 ML 技術設計自動化工具，優化內部作業流程與決策效率。落地與維運：負責模型的容器化 (Docker) 封裝與部署，監控模型在正式環境的表現，確保系統的高可用性。技術顧問：與跨部門團隊協作，向非技術人員解釋 AI 模型的應用場景與邊界 (Limitations)。

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.

【Company Overview】Recognized by Taiwan government as one of "Next Big" startups, GoFreight is harnessing the power of cutting-edge technologies to revolutionize the global freight-forwarding industry. We are searching for a self-motivated AI Intern who is eager to take the lead on crucial projects and drive innovative solutions.【Responsibilities】- Build AI prototypes that solve actual business problems. Think chatbots, document processing pipelines, routing systems. Most projects go from idea to working product in a few weeks.- Own your projects end-to-end: scoping, building, evaluating, shipping. The things you build will be used by real people.- Work with senior engineers and product managers to figure out what to build. Once the direction is clear, you run with it.- Evaluate and improve AI pipelines. That means measuring prompt quality, extraction accuracy, and whether the outputs are actually reliable.- Look for places where LLMs can replace tedious manual work in freight operations. Try things. If they don't work, write up what you learned and move on.- Put LLM-based tools into existing systems and workflows to automate what's slow or repetitive.- Write things down. Good documentation means the next person can pick up where you left off.- Keep up with what's happening in AI and share what you find. We like people who show up with ideas.

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. Key Responsibilities1. 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. -

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）

1.針對半導體機台智能製造 IoT Data Connecting 相關技術的建構與整合 2.IoT 網路Data Connecting 技術與開發 3.IT System 整合開發 4.網頁設計與架構技術開發 5.IoT系統設計與自動化維運開發 6.GenAI 應用專案開發 7.日管維運、建立SOP、維持系統穩定 8.持續精進強化專業技能，提升競爭力

GenAI 技術理解與應用: 熟悉 LLM、RAG、embedding、fine-tune、prompt engineering 等 genAI 相關技術方法。1.資料分析與演算法/模型優化: 能處理自然語言, 影像及數值類等多模態數據, 具備應用向量資料庫、rerank 技術, 並能進行效能分析與改善。2.系統與平台建置: 具備雲端或地端部署經驗, 熟悉 Kubernetes/Docker, 能開發與整合 GenAI 平台或 workflow 工具 (如 n8n , dify,langflow)。3.軟體工程與架構設計: 具備良好工程基礎, 熟悉 API 開發、模組化設計、CI/CD，自動化測試與維運。4.自動化與流程整合: 具備 RPA 或流程自動化經驗與影像處理AOI經驗, 能將 GenAI 技術串接內部系統與業務流程。

1.依部門訂立之計劃，落實執行，達成目標，當責 GenAI 平台及應用專案開發，協助各部門增進效率，解決部門痛點，提昇公司長期競爭力 2.GenAI 系統平台建置 3.GenAI 應用專案開發 4.日管維運、建立SOP、維持系統穩定 5.持續精進強化專業技能，提升競爭力

1.針對半導體機台智能製造 IoT Data Connecting 相關AI技術的建構與整合 2.IoT 硬體Data Connecting 技術與開發 3.IT System 整合開發 4.熟悉半導體生產機台 5.IoT 行動裝置App 設計 6.AI 應用專案開發 7.日管維運、建立 SOP、維持系統穩定 8.持續精進強化專業技能，提升競爭力

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-blogAbout the roleWe 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. 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. -