SWAG

負責協助平台創作者從「基礎培訓 → 經營成效提升」的全流程。 核心任務是協助創作者快速熟悉平台、提升內容質量與數量、建立穩定營運節奏，並透過數據洞察提出實際可行的成長建議。 你將成為創作者的第一線支持者，也是產品與營運團隊的重要橋樑，幫助創作者在短期內看見成效、提升留存與營收。

About the RoleAs a Tech Lead, you will spearhead an engineering pod, taking full ownership of project delivery and system evolution. You will bridge the gap between product requirements and technical excellence, ensuring our systems remain scalable and stable while migrating from legacy architectures to modern microservices.What you'll Do• Project Leadership: Drive end-to-end project lifecycles, manage timelines, and unblock development bottlenecks to ensure timely delivery.• Architectural Evolution: Plan and execute migrations from legacy systems to microservices, ensuring high availability and scalability.• Operational Authority: Partner with DevOps to review production changes and act as a Tier-2 responder for critical incidents (P0/P1).• Quality Mentorship: Enforce rigorous Code Reviews, oversee the output quality of our outsourcing partners (SotaTek), and mentor L2/L3 engineers.7

Description SWAG is building AI-native product capabilities powered by LLM-based agents and multi-modal intelligence. These systems are not experimental prototypes — they operate in production, interact with real users, and directly impact product experiences and business outcomes. We are looking for a AI Engineer to design, build, and operate AI agent systems that integrate large language models, vision models, internal data platforms, and external tools. You will work closely with Data, Product, and Engineering teams to deliver scalable, reliable, and observable AI systems. Responsibilities 1. Design and implement LLM-based AI agents that support multi-step reasoning, tool / function calling, and contextual decision-making. 2. Build and maintain agent workflows that interact with: Internal data platforms and services External APIs and third-party tools Product events and user signals 3.Integrate computer vision and multi-modal models (vision + language) into agent workflows and product features. 4. Apply vision models for use cases such as content understanding, classification, moderation, or personalization. 5. Own the end-to-end lifecycle of AI systems, including: Prompt and agent versioning Model selection, upgrades, and deprecation Online inference services and deployment 6. Implement LLMOps practices, including: Automated evaluation of LLM outputs Monitoring quality, latency, usage, and cost Identifying and mitigating real-world failure cases 7. Collaborate with Data Engineers to integrate AI systems into existing infrastructure, CI/CD pipelines, and observability platforms. 8. Work closely with Data Scientists, Product Managers, and Software Engineers to translate business requirements into production-ready AI solutions.

本職位負責公司整體電商事業的策略規劃與營運成效，涵蓋線上通路布局、數位行銷決策、團隊管理與跨部門協作。需以數據為核心，整合行銷、產品、技術與供應鏈資源，推動營收成長並提升長期獲利能力，同時持續評估並導入新的電商營運模式，以維持市場競爭力。職位使命SWAG 擁有全台最強的成人內容流量池。你需要從零打造 SWAG 的電商事業體，將龐大的流量轉化為實際營收。你不需要煩惱流量哪裡來，核心任務是打造最強的供應鏈、優化毛利結構，並確保物流與金流的極致隱私體驗。 工作內容 1. 供應鏈與選品策略 (40%) —— 這是最重要的OEM/ODM 開發： 直接對接中國/日本成人用品工廠，開發 SWAG 自有品牌商品（Private Label），將毛利率拉高至 60% 以上。選品規劃：根據平台用戶畫像，從低價耗材到高單價智能玩具，規劃高轉化率的選品矩陣。庫存管理：建立庫存週轉模型，確保熱銷品不斷貨、滯銷品能快速出清，將資金使用效率最大化。2. 營運與物流體驗 (30%)隱私物流建置：對接第三方物流與超商體系，確保「包裝極度隱密」與「取貨流程順暢」，解決用戶購買的最大心理障礙。金流風控：處理高風險產業的支付渠道對接，確保刷卡成功率並控制手續費成本。商城搭建：與RD團隊協作，優化站內商城的使用者路徑，提升下單轉化率。3. 商業變現與創作者連動 (30%)直播帶貨機制：設計主播/女優的分潤與激勵機制，讓創作者願意在直播中主動推銷商品。活動策展：結合節慶與平台大型活動（如年度盛典），策劃促銷方案。數據分析：監控電商關鍵指標，如AOV、CR、LTV等數據，並依據數據調整營運策略。

你不是在辦活動，你是在「設計一套能自我轉動的激勵系統」。此職位負責主導 SWAG 創作者端的活動機制設計。需具備強大的產品化思維，將平台資源（流量、功能權限、榮譽體系等）轉化為具備吸引力的活動產品，並引導創作者提升內容供需、活躍度及留存率。你將與產品、數據、Ops 團隊緊密協作，透過「資源槓桿」達成最佳營運目標。

我們正在尋找一位 細心、負責、具良好執行力 的行政工讀生，協助支援公司日常行政與辦公室運作。此職位將協助文件整理、資料建檔、庶務行政及會議安排等相關工作，確保行政流程順暢，並與人資及行政團隊密切合作，維持辦公環境與行政運作的穩定性。

我們正在尋找一位直播平台活動營運專員，您將成為平台營收成長的關鍵推手！透過深度用戶洞察、創意內容策展與跨部門協作，規劃並執行高轉換的檔期活動，推動點數購買與使用需求持續突破，助力平台在競爭激烈的直播生態中脫穎而出。 工作內容 1. 檔期活動執行透過跨部門協作，從活動主題發想、檔期排程到資源調度，完整統籌大型促銷節慶（如雙11、年終購物節）或自製主題檔期（如品牌週、會員專屬日），確保活動目標與平台整體營運策略一致。深度剖析用戶購買行為軌跡（包含瀏覽路徑、轉換漏斗、複購週期），結合平台熱門品類與時令趨勢，規劃與執行具高吸引力的檔期活動方案。活動上線後即時監控數據表現，快速迭代優化（如調整獎勵機制、推播文案、曝光時段）。2. 內容策展依檔期主題，從平台內容庫中挑選並策展合適的創作者作品或直播內容（包含短影音、直播預告），於首頁 Banner、專屬活動頁面、個人化推送中展出，確保內容與活動高度相關，提升用戶點擊率與轉換率。設計活動內互動機制（限時任務、排行榜、抽獎模組），強化用戶參與黏著度與點數消耗意願。3. 跨部門協作與營收洞察報告創作者部門緊密協作，共同規劃檔期活動內容，推升點數購買及使用需求。活動復盤，產出結構化洞察報告，包含：數據拆解、用戶分群分析、成功/失敗因素。建立活動 SOP 與知識庫，降低新檔期上線門檻，加速團隊迭代效率。

Responsibilities Communicate with relevant stakeholders and develop application features within our services.Contribute quality code to the code base.Optimize code for performance and cost.Debug application bugs in a timely fashion.

我們正在尋找一位具備數據分析與自動化能力的營運專家，負責建立並優化平台營運流程，提升效率和用戶轉換率。您將透過數據分析、流程自動化與跨部門協作，確保平台的流暢運作，並推動業務成長。 工作內容 用戶成長執行與數據觀察：運用 Mixpanel 和 BigQuery 分析用戶行為，將數據洞察轉化為可執行的營運方案，涵蓋從方案規劃、資源分配到執行監控的全流程。分析並優化用戶轉換漏斗及旅程，透過 A/B 測試提升轉換率和用戶滿意度。監控關鍵指標（KPI），提供數據驅動的營運策略與改進建議。營運流程自動化：使用 n8n 或類似工具，設計並管理自動化工作流程，建立自動化用戶旅程、簡化重複性任務。串接內部系統（如：站內訊息發送工具、E-mail 發送工具、BigQuery 等等），提升整體營運效率。平台管理與協作：負責平台日常營運，確保用戶體驗順暢並符合業務目標。與產品、技術團隊緊密合作，共同推動功能優化。

At SWAG Live's data team, our mission is to democratize data by building a self-service data platform. We aim to empower internal teams to access, interpret, and derive valuable insights from data effectively.As a Data Scientist in this role, you will collaborate with a multidisciplinary team of engineers and analysts to tackle diverse challenges using quantitative techniques such as statistical analysis and machine learning. You will work with large, complex event-based datasets, conduct exploratory data analysis (EDA), define requirements, and develop deploy models. We are particularly seeking a data scientist with experience in building customized recommendation models and a strong interest in product-focused machine learning development. Responsibilities Leverage state-of-the-art algorithms to build fully customized recommenders and other growth models.Design, deploy and maintain all components necessary for modeling, including feature engineering, automatic model training tuning and engineering toolchains.Create a comprehensive monitoring framework to evaluate model performance and provide actionable insights to drive business growth, focusing on awareness conversion and transactions.Understand stakeholder business requirements and design end-to-end machine learning/AI solutions that are effective, practical, and robust in addressing business challenges.Collaborate closely with data engineers and backend engineers to develop scalable systems.Communicate efficiently with cross-functional teams, promote the implementation of strategic applications, and drive continuous optimization.