About Appier 

Appier (TSE: 4180) is an AI-native Agentic AI as a Service (AaaS) company that empowers businesses to create value through cutting-edge AdTech and MarTech solutions. Founded in 2012 with the vision of “Making AI Easy by Making Software Intelligent,” Appier helps businesses turn AI into ROI through its Ad Cloud, Personalization Cloud, and Data Cloud—each powered by Agentic AI that enables autonomous, adaptive, and real-time decision-making. Today, Appier operates 17 offices across APAC, the US, and EMEA, and is listed on the Tokyo Stock Exchange. Learn more at www.appier.com.

About the role

We are looking for a Product Manager who combines sharp product instincts with real software engineering fluency to own and drive complex, multi-system SaaS products within Appier's Ad Cloud platform.

You will be responsible for defining, designing, and shipping products that span multiple backend systems, API integrations, and cross-functional stakeholders. This isn't a wireframe-and-handoff role, you'll write functional specs with API-level precision, navigate system-of-record ownership across 5+ services, and make architectural trade-off decisions alongside engineering leads.

You'll also be expected to leverage modern AI agent tools (Claude Code, Cursor, Copilot, LLM-based automation) as a core part of your PM workflow, from rapid prototyping of product specs and whitepaper-grade documentation, to data analysis and stakeholder communication. We believe PMs who can wield AI tools effectively operate at 3-5x the output of those who don't.

What You Will Do

Product Design & System Architecture [Core]

- Own the end-to-end product lifecycle for complex SaaS modules that orchestrate across multiple dependent systems (e.g., creative management, tracking, campaign operations, entity hierarchies)
- Author whitepaper-grade product design documents that cover system context maps, interoperability flows, entity hierarchies, state machines, and functional matrices, with enough precision for engineers to implement without ambiguity
- Define system-of-record ownership, API contracts, and data flow directions across multi-system architectures
- Make scope and trade-off decisions across milestones, balancing feature completeness against release velocity

Technical Specification & Execution [Technical]

- Write functional specifications at the API-level: request/response payloads, error handling contracts, idempotency requirements, and cascade/inheritance rules
- Define status matrices with explicit state transitions, ownership boundaries, and cross-system sync behaviors (one-way sync vs. bidirectional, lock/unlock mechanisms)
- Decompose large product initiatives (70+ features) into staged delivery plans with clear dependency graphs, checkpoint gates, and decision trackers for blocking items
- Work directly with engineering teams on implementation trade-offs, you read code, understand database schemas, and can evaluate technical proposals on their merits

AI-Augmented Product Management [AI]

- Use AI agent tools (LLM coding assistants, AI research agents, prompt-driven automation) as daily productivity multipliers for spec writing, competitive analysis, data modeling, and stakeholder communication
- Evaluate how AI/LLM capabilities can be embedded into the product itself, identifying where intelligent automation, content generation, or predictive features create user value
- Prototype product concepts rapidly using AI-assisted development tools, reducing the spec-to-prototype cycle from weeks to days

Cross-Functional Leadership [Leadership]

- Serve as the central product authority across engineering, design, customer success, and sales teams, translating business requirements into precise technical specifications and vice versa
- Drive decision-making on blocking items by framing trade-offs clearly, proposing options with pros/cons, and driving resolution with stakeholders
- Manage product rollouts with permission controls, feature gating, and phased go-live strategies for internal and external users

What You Will Need

[Minimum qualifications]

• Experience: 2-5 years as a Product Manager, Technical PM, or Software Engineer transitioning to product, with a track record of shipping multi-system SaaS/AI-native products
• Engineering Fluency: Solid software development background, you can read and reason about code (any language), understand REST API design, database schemas, state machines, and system architecture diagrams. Prior hands-on engineering experience is strongly preferred
• Spec Quality: Demonstrated ability to write precise technical specifications, not just user stories, but API contracts, entity relationship models, status transition matrices, and interoperability flow diagrams
• Scope Management: Experience with complex product decomposition, breaking down a 50+ feature scope into staged milestones with dependency tracking, decision logs, and blocking item resolution
• AI Tool Fluency: Working fluency with AI/LLM tools, you actively use AI coding assistants, LLM-based research tools, or prompt engineering in your daily workflow. You understand what these tools can and cannot do
• Communication: Excellent communication skills in both English and Mandarin, with the ability to produce clear written artifacts (whitepapers, PRDs, playbooks) that serve as single-source-of-truth references for cross-functional teams
• AI Agent Frameworks: Hands-on experience with modern AI agent frameworks (Claude Code, Cursor, Codex ect) or building AI-powered product features

[Nice to have]

• Background in AdTech, MarTech, or enterprise SaaS platforms with multi-tenant architectures
• Experience managing products that involve third-party system integrations and API partnerships
• Familiarity with Agile/Scrum delivery, but comfort operating in a hybrid model where staged milestone planning coexists with sprint-level execution

Appier is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

#LI-AK1

介紹 GNTC 團隊

Dcard GNTC 團隊致力於打造企業級 AI 工作平台，讓企業都能透過 AI 提升工作效率、創造更高價值。
我們相信 AI 不只是工具，更是每個團隊成員的智能助手，能夠深度整合企業資料與流程，協助團隊做出更精準的決策、產出更高品質的成果。我們提供全方位的企業 AI 工作平台，使企業夥伴可以提升決策與洞察 、加速企劃產出 ，並且能透過 Agent 自動化處理例行任務，讓團隊專注於更具創造性與決策價值的工作。
為了達成這個目標，我們需要 Fullstack Engineer 加入我們，負責 Dcard GNTC 企業 Agent 平台的前後端開發與系統架構設計。你將端到端打造完整的生成式 AI 應用，為企業客戶提供優質的產品體驗。

你將在團隊參與⋯

• 開發 Dcard GNTC Agent 平台的前後端完整功能
  • 設計並實作 AI Chat 介面、Dashboard、管理後台等前端應用
  • 開發 MCP Server、API、AI Workflow 等後端服務
  • 整合 LLM、RAG 等 AI 技術棧
  • 優化前端使用者體驗與後端系統效能
• 根據產品規劃與客戶需求，快速交付完整的技術方案
  • 參與技術方案討論，從前端到後端提供完整的實作建議
  • 將產品需求轉化為清晰的前後端功能與使用者體驗
  • 根據時程、成本、技術可行性平衡前後端開發優先順序
• 持續改善產品體驗與技術架構
  • 優化前端互動體驗、效能與可用性
  • 強化後端系統的穩定性、可擴展性與維護性
  • 建立可重用的 UI 元件與後端模組
  • 改善開發工具、CI/CD 與部署流程
• 技術研究與知識分享
  • 持續學習 Agent 相關知識與前後端新技術，評估並導入適合的技術
  • 在團隊內部進行技術分享與文件撰寫

我們期待看到一個優秀的 Full Stack Engineer 具備以下能力

• 5 年以上 Web / Full Stack 開發經驗，能獨立交付完整產品功能
• 具備良好的產品工程思維，能在需求不完全清楚時完成拆解、推進與落地
• 熟悉以下技術：
  • 熟悉 AI SDK
  • MCP（Model Context Protocol）開發與整合經驗
    • 理解 MCP 的架構與運作原理
    • 實際開發過 MCP Server 或 Client
    • 能設計企業級 MCP 整合方案
    • LLM 應用開發：深入理解 Prompt Engineering、Context Engineering、RAG（Retrieval-Augmented Generation）
    • LLM-based Workflow Automation 與 AI Agent 開發經驗
  • 前端開發能力
    • 熟悉 React
    • 精通 TypeScript
    • 熟悉 Next.js 或類似 SSR/SSG 框架
    • 能設計並實作優秀的 AI Chat 介面與互動體驗
    • 理解 WebSocket、Server-Sent Events (SSE) 等即時通訊技術
    • 熟悉 Tailwind CSS 或其他 CSS 框架
  • 後端開發能力
    • 精通 TypeScript
    • 熟悉 Python
    • REST API 設計與開發
    • Relational / NoSQL Database 使用經驗
    • 能熟練操作 Linux（部署、自動化腳本）
    • 理解 Networking 基礎（DNS、TCP/IP、WebSocket、SSE、Load Balancing）
    • Cloud-Native & Container（Kubernetes、Docker）

如果你擁有以下能力，那就更棒了！

• 熟悉 LangChain、LlamaIndex 或其他 AI 開發框架
• Vector Database 使用經驗（如 Pinecone、Qdrant、Weaviate、Milvus）
• 前端進階能力
• 熟悉 Webview 或跨平台開發經驗
• 有 UI/UX 設計敏感度，能與設計師高效協作
• 熟悉 Zustand、Jotai 或其他狀態管理工具
• AI 相關進階能力
• 具備 AI SDK 實際開發經驗
• OpenAI Function Calling、Assistants API 深度使用經驗
• LoRA 等模型訓練與優化經驗
• 有開發 AI Agent 或 Multi-Agent System 經驗
• 對 Embedding Models、Semantic Search 有深入理解
• Streaming Response 處理經驗（前後端）
• 其他
• 在技術社群有活躍貢獻（開源專案、技術文章、Conference 演講）

薪資待遇

• 面議

注意事項

• 如果團隊覺得有機會合作，一起讓 Dcard 往更高的目標邁進，我們會儘速聯絡您！
• 若不符合職位需求的面試者，將不另行通知。
• 如果尋找到合適的人選加入，職缺可能會提前關閉。
• 如果人選在申請過程中提供不實資訊，Dcard 有權取消雇用提議。
• 在 Dcard，每位夥伴都是平等的，我們重視每個人的多元性。Dcard 致力於打造一個多元與共融的環境，尊重並且包容每一位夥伴。我們相信，平等與多元能夠帶來無限創新的可能。因此，我們的招聘流程提供每位應徵者平等的就業機會，並落實無歧視的就業環境

狄卡科技股份有限公司及其關係企業基於「員工招募」之目的，將蒐集您的「識別類、特徵類、社會情況、教育、考選、技術或其他專業、受僱情形」等個人資料，或您主動為求職目的而提供的資料（履歷、自傳等），以在招募期間及地區內，作為本次招募作業審核、評選、聯繫之用。如您未獲錄取，我們仍將保留您的資料以便向您通知新的職缺機會。

您得以致電狄卡科技股份有限公司及其關係企業（電子信箱：hr.ta@dcard.cc）行使請求查閱、補充、更正；請求提供複製本；請求停止蒐集、處理、利用；請求刪除個人資料等權利。如您未正確、完整提供各項資料，將可能影響聘用審核、評選或聯繫。

介紹 GNTC 團隊

Dcard GNTC 團隊致力於打造企業級 AI 工作平台，讓企業都能透過 AI 提升工作效率、創造更高價值。
我們相信 AI 不只是工具，更是每個團隊成員的智能助手，能夠深度整合企業資料與流程，協助團隊做出更精準的決策、產出更高品質的成果。我們提供全方位的企業 AI 工作平台，使企業夥伴可以提升決策與洞察 、加速企劃產出 ，並且能透過 Agent 自動化處理例行任務，讓團隊專注於更具創造性與決策價值的工作。
為了達成這個目標，我們需要 Forward Deployed Engineer 加入我們，你將駐點或遠端與企業客戶深度合作，從業務需求拆解、原型設計，到雲端部署與維運，端到端交付生成式 AI 解決方案。歡迎加入這個熱愛挑戰的技術團隊，一起打造被千萬人喜愛與使用的產品！

你將在團隊參與⋯

• 基於 Dcard GNTC Agent 平台設計技術方案，並以現場 (on site) 或遠端 (remote) 的方式與企業客戶深度合作，從業務需求拆解、原型設計，到雲端部署與維運，端到端交付生成式 AI 解決方案。
• 與客戶 BU／IT 團隊進行訪談，定義 Problem Statement 與 KPI
• 負責理解用戶現行流程與痛點，並提出可行的技術解決方案。以 TypeScript（或 Python）實作 API／MCP／Agent 等 Workflow，協助用戶在其既有系統中導入與整合。
• 撰寫技術白皮書、實踐手冊 (Playbook)，回饋產品 Roadmap 與最佳實踐 (best practice)

我們期待看到一個優秀的 Forward Deployed Engineer 具備以下能力

• 3+ 年軟體開發與系統整合經驗
• 熟悉以下技術：

1. Python、Golang 或 TypeScript 任一
2. 理解 MCP（Model Context Protocol）規格，並具實際開發或整合經驗
3. 能熟練操作 Linux（部署、自動化腳本）
4. 理解基本 Networking（DNS、TCP/IP、Load Balancing、Service Mesh 更佳）
5. Cloud-Native & Container（Kubernetes、Docker）

• 優秀問題拆解、客戶溝通能力，能以中英文撰寫文件並進行技術簡報

如果你擁有以下能力，那就更棒了！

• REST / gRPC API 設計
• Relational / NoSQL / Vector DB
• Prompt Engineering、Context Engineering、RAG、LLM-based Workflow Automation
• 顧問式專案或駐點交付經驗（Forward Deployed / Professional Services）
• 熟悉 ISO 27001、GDPR、SOC 2、HIPAA 等資安與合規需求
• 使用 LangChain、LlamaIndex、OpenAI Function Calling、Fine-tuning / LoRA 經驗
• ML Ops / Data Ops / Infra-as-Code 專案實戰

薪資待遇

• 面議

注意事項

• 如果團隊覺得有機會合作，一起讓 Dcard 往更高的目標邁進，我們會儘速聯絡您！
• 若不符合職位需求的面試者，將不另行通知。
• 如果尋找到合適的人選加入，職缺可能會提前關閉。
• 如果人選在申請過程中提供不實資訊，Dcard 有權取消雇用提議。
• 在 Dcard，每位夥伴都是平等的，我們重視每個人的多元性。Dcard 致力於打造一個多元與共融的環境，尊重並且包容每一位夥伴。我們相信，平等與多元能夠帶來無限創新的可能。因此，我們的招聘流程提供每位應徵者平等的就業機會，並落實無歧視的就業環境

狄卡科技股份有限公司及其關係企業基於「員工招募」之目的，將蒐集您的「識別類、特徵類、社會情況、教育、考選、技術或其他專業、受僱情形」等個人資料，或您主動為求職目的而提供的資料（履歷、自傳等），以在招募期間及地區內，作為本次招募作業審核、評選、聯繫之用。如您未獲錄取，我們仍將保留您的資料以便向您通知新的職缺機會。

您得以致電狄卡科技股份有限公司及其關係企業（電子信箱：hr.ta@dcard.cc）行使請求查閱、補充、更正；請求提供複製本；請求停止蒐集、處理、利用；請求刪除個人資料等權利。如您未正確、完整提供各項資料，將可能影響聘用審核、評選或聯繫。

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 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. 

Qualifications

• Experience: 5+ years in Machine Learning Engineering, with specific experience in leading technical projects or mentoring engineers.
• Communication & Business Acumen: Exceptional ability to distill complex technical topics (e.g., compute complexity, infrastructure costs) into clear, business-relevant insights for decision-makers.
• MLOps Proficiency: Proven experience in optimizing ML pipelines and infrastructure. Familiarity with tools like MLflow, Kubeflow, Airflow, and Data Versioning tools (DVC, etc.).
• Engineering First: Proficient in Python, Docker, and Kubernetes. You treat ML models as software artifacts that need testing and version control.
• NLP & LLM Expertise: Experience with Transformer architectures, Embeddings, and LLM fine-tuning. Familiarity with frameworks like PyTorch, Hugging Face, and vLLM.
• Language Support: Experience processing or fine-tuning models for multi-lingual environments. 

Nice to Have

• Multimodal Expertise: Experience working with Multimodal models (Image-to-Text, Text-to-Image, VLMs like CLIP, LLaVA).
• Security Awareness: Understanding of GenAI security threats (e.g., Prompt Injection).
• High-Performance Computing: Experience optimizing inference speed (quantization, distillation, vLLM) for real-time applications.
• Vector Database: Experience with Vector DBs for RAG applications. 

Why Join Us? 

• Innovative Environment: Be part of a company at the forefront of technology to provide security in GenAI, with opportunities to work on groundbreaking projects. 
• Growth Opportunities: Take your career to new heights with our career development programs and growth-focused culture. 
• Dynamic Team: Join a multi-cultural and dynamic team of dedicated professionals who inspire and support each other. 
• Compensation: Competitive salary and benefits package, commensurate with experience and performance. 

Application Process 

• If you're ready to embark on this exciting journey and contribute to shaping the future of GenAI security, please submit your resume outlining your relevant experience and motivation for applying.
• If you prefer a direct connection or have specific questions about our vision, feel free to reach out to our Co-founder, Alvin Kwock, via LinkedIn. 

介紹 GNTC 團隊

Dcard GNTC 團隊致力於打造企業級 AI 工作平台，讓企業都能透過 AI 提升工作效率、創造更高價值。
我們相信 AI 不只是工具，更是每個團隊成員的智能助手，能夠深度整合企業資料與流程，協助團隊做出更精準的決策、產出更高品質的成果。我們提供全方位的企業 AI 工作平台，使企業夥伴可以提升決策與洞察 、加速企劃產出 ，並且能透過 Agent 自動化處理例行任務，讓團隊專注於更具創造性與決策價值的工作。
為了達成這個目標，我們需要 Senior Backend Engineer 加入我們，負責 Dcard GNTC 企業 Agent 平台的後端架構設計、AI 模型整合與系統開發。你將專注於打造穩定、可擴展的生成式 AI 後端服務，為企業客戶提供強大的技術支撐。

你將在團隊參與⋯

• 設計與開發 Dcard GNTC Agent 平台的後端系統
  • 設計並實作 MCP Server、API、AI Workflow
  • 整合 LLM、Vector Database、RAG 等 AI 技術
  • 優化系統效能、可靠性與可擴展性
• 根據產品規劃與客戶需求，設計並實作技術方案
  • 參與技術方案討論，根據時程、成本、技術可行性提出建議
  • 將產品需求轉化為清晰的技術架構與實作
• 持續改善平台架構與開發體驗
  • 全面瞭解整體系統架構，識別並優化架構
  • 建立可復用的技術元件與最佳實踐
  • 改善 CI/CD、監控、部署流程
• 技術研究與知識分享
  • 持續學習 Agent 相關知識與新技術，評估並導入適合的技術
  • 在團隊內部進行技術分享與文件撰寫

我們期待看到一個優秀的 Senior Backend Engineer 具備以下能力

• 5+ 年軟體開發經驗，具備獨立負責後端系統的設計與交付能力
• 熟悉以下技術：
  • 精通 TypeScript
  • 熟悉 Python
  • MCP（Model Context Protocol）開發與整合經驗
    • 理解 MCP 的架構與運作原理
    • 實際開發過 MCP Server 或 Client
    • 能設計企業級 MCP 整合方案
  • LLM 應用開發：深入理解 Prompt Engineering、Context Engineering、RAG（Retrieval-Augmented Generation）
    • LLM-based Workflow Automation 與 AI Agent 開發經驗
  • LangChain、LlamaIndex、Vercel AI SDK 或其他 AI 開發框架
  • REST / gRPC API 設計與開發
  • Relational / NoSQL Database 使用經驗
  • Vibe Coding
  • 能熟練操作 Linux（部署、自動化腳本）
  • 理解 Networking 基礎（DNS、TCP/IP、WebSocket、SSE、Load Balancing）
  • Cloud-Native & Container（Kubernetes、Docker）

如果你擁有以下能力，那就更棒了！

• OpenAI Function Calling、Assistants API 深度使用經驗
• Vector Database 使用經驗（如 Pinecone、Qdrant、Weaviate、Milvus）
• 有開發 AI Agent 或 Multi-Agent System 經驗
• 熟悉 Service Mesh（如 Istio、Linkerd）
• Infrastructure-as-Code（Terraform、Pulumi）專案經驗
• 在技術社群有活躍貢獻（開源專案、技術文章、Conference 演講）

薪資待遇

• 面議

注意事項

• 如果團隊覺得有機會合作，一起讓 Dcard 往更高的目標邁進，我們會儘速聯絡您！
• 若不符合職位需求的面試者，將不另行通知。
• 如果尋找到合適的人選加入，職缺可能會提前關閉。
• 如果人選在申請過程中提供不實資訊，Dcard 有權取消雇用提議。
• 在 Dcard，每位夥伴都是平等的，我們重視每個人的多元性。Dcard 致力於打造一個多元與共融的環境，尊重並且包容每一位夥伴。我們相信，平等與多元能夠帶來無限創新的可能。因此，我們的招聘流程提供每位應徵者平等的就業機會，並落實無歧視的就業環境

狄卡科技股份有限公司及其關係企業基於「員工招募」之目的，將蒐集您的「識別類、特徵類、社會情況、教育、考選、技術或其他專業、受僱情形」等個人資料，或您主動為求職目的而提供的資料（履歷、自傳等），以在招募期間及地區內，作為本次招募作業審核、評選、聯繫之用。如您未獲錄取，我們仍將保留您的資料以便向您通知新的職缺機會。

您得以致電狄卡科技股份有限公司及其關係企業（電子信箱：hr.ta@dcard.cc）行使請求查閱、補充、更正；請求提供複製本；請求停止蒐集、處理、利用；請求刪除個人資料等權利。如您未正確、完整提供各項資料，將可能影響聘用審核、評選或聯繫。

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 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. 

Qualifications

• Experience: 5+ years in Machine Learning Engineering, with specific experience in leading technical projects or mentoring engineers. 

• Communication & Business Acumen: Exceptional ability to distill complex technical topics (e.g., compute complexity, infrastructure costs) into clear, business-relevant insights for decision-makers. 

• MLOps Proficiency: Proven experience in optimizing ML pipelines and infrastructure. Familiarity with tools like MLflow, Kubeflow, Airflow, and Data Versioning tools (DVC, etc.). 

• Engineering First: Proficient in Python, Docker, and Kubernetes. You treat ML models as software artifacts that need testing and version control. 

• NLP & LLM Expertise: Experience with Transformer architectures, Embeddings, and LLM fine-tuning. Familiarity with frameworks like PyTorch, Hugging Face, and vLLM. 

• Language Support: Experience processing or fine-tuning models for multi-lingual environments. 

Nice to Have

• Multimodal Expertise: Experience working with Multimodal models (Image-to-Text, Text-to-Image, VLMs like CLIP, LLaVA). 

• Security Awareness: Understanding of GenAI security threats (e.g., Prompt Injection). 

• High-Performance Computing: Experience optimizing inference speed (quantization, distillation, vLLM) for real-time applications. 

• Vector Database: Experience with Vector DBs for RAG applications. 

Other Benefits 

To us, people are our greatest asset, and we are more than happy to invest in employees! We create a healthy work atmosphere and provide you with the tools and support for doing your job successfully. With a culture of flexibility and transparency, we believe there should be no barriers, and everyone’s contributions matter. 

Work Life Balance is a must  

• 15 days annual leaves (pro-rata for partial month at first year) 
• 5 days full-pay sick leaves, 3 days menstrual leaves 
• Health check subsidy 
• Ergonomic-design chair and fully-equipped devices for work
• Hybrid remote work and flexible working hour.

Grow together & keep learning

• Conferences & external subsidy 
• Learning clubs to share technical skill (e.g: Frontend/Backend tech sharing, Blockchain...etc) 

Work Hard, Play even Harder 

• Various entertainment & sports clubs, attend basketball clubs today, and play board game tomorrow! 
• Snacks & beverage to refill your energy anytime 

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.

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.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.
• Proven experience in training classifiers and fine-tuning transformer-based models.
• Strong understanding of tokenization techniques, embedding models, and vector representations.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers.
• Familiarity with hyperparameter tuning and model configuration best practices.
• Experience working with large-scale datasets and building reproducible ML pipelines.
• Fluent in Mandarin; proficiency in English is an advantage.

Desired Skills

• Knowledge of cloud platforms (AWS, Azure, GCP) and containerized deployment (Docker, Kubernetes).
• Experience with LLM prompting and fine-tuning is a plus.
• Basic understanding of data engineering practices (ETL, data validation, feature engineering).
• Exposure to AI safety, security, or bias mitigation techniques is an advantage.

Other Benefits 

To us, people are our greatest asset, and we are more than happy to invest in employees! We create a healthy work atmosphere and provide you with the tools and support for doing your job successfully. With a culture of flexibility and transparency, we believe there should be no barriers, and everyone’s contributions matter. 

Work Life Balance is a must  

• 15 days annual leaves (pro-rata for partial month at first year) 
• 5 days full-pay sick leaves, 3 days menstrual leaves 
• Health check subsidy 
• Ergonomic-design chair and fully-equipped devices for work
• Hybrid remote work and flexible working hour.

Grow together & keep learning

• Conferences & external subsidy 
• Learning clubs to share technical skill (e.g: Frontend/Backend tech sharing, Blockchain...etc) 

Work Hard, Play even Harder 

• Various entertainment & sports clubs, attend basketball clubs today, and play board game tomorrow! 
• Snacks & beverage to refill your energy anytime