쿠팡 광고 부문에서 머신러닝 기술 전략을 이끌 Senior Staff MLE를 채용합니다. 10년 이상의 ML 경력과 광고 도메인 전문성이 필수이며, LLM 및 생성형 추천 모델을 광고 시스템에 도입하는 핵심 역할을 수행합니다. 대규모 분산 시스템 설계 경험과 팀 리딩 역량을 갖춘 분을 찾습니다.
We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did I ever live without Coupang?” Born out of an obsession to make shopping, eating, and living easier than ever, we are collectively disrupting the multi-billion-dollar commerce industry from the ground up and establishing an unparalleled reputation for being leading and reliable force in South Korean commerce.
We are proud to have the best of both worlds — a startup culture with the resources of a large global public company. This fuels us to continue our growth and launch new services at the speed we have been since our inception. We are all entrepreneurs surrounded by opportunities to drive new initiatives and innovations. At our core, we are bold and ambitious people that like to get our hands dirty and make a hands-on impact. At Coupang, you will see yourself, your colleagues, your team, and the company grow every day.
Our mission to build the future of commerce is real. We push the boundaries of what’s possible to solve problems and break traditional tradeoffs. Join Coupang now to create an epic experience in this always-on, high-tech, and hyper-connected world.
制定广告ML中长期路线图（1-3年），涵盖 Recall、Ads Relevance、Ranking、Auction、Auto Bidding 等核心领域，驱动技术方向与业务目标的深度对齐，指导团队完成业务指标。
主导LLM/生成式推荐（Generative Recommendation）技术在广告系统中的研究与落地，探索以生成式模型替代传统"召回→粗排→精排→重排"多阶段级联架构的可行性路径。包括但不限于：Generative Retrieval（如 DSI、TIGER、GENRE）、Generative Re-ranking、以及基于 Sequence-to-Sequence 的用户行为建模等。
有 RLHF（Reinforcement Learning from Human Feedback） 或 RLAIF 在推荐/广告模型对齐中的经验，能够将业务目标（Revenue、Relevance、UX）作为奖励信号优化生成策略。
作为广告 ML 领域的技术权威，与广告业务、产品、数据工程、基础架构等多个部门的领导层深度协作，识别高价值技术机会，推动战略级项目落地。
主导跨团队的复杂技术项目（如全链路模型升级、在线/离线一体化建设），协调多团队资源，确保项目按时高质量交付。
指导和培养 Staff MLE 及高级工程师，通过技术评审、架构讨论、1:1 辅导等方式提升团队技术能力。 - 主导技术招聘标准制定和面试流程，建设高水平 ML 技术团队。
熟练掌握 TensorFlow、PyTorch 等深度学习框架，具备扎实的算法基础和系统设计能力。
深入理解 Recall（召回）、Relevance（相关性）、Ranking（排序）、Auction（拍卖）、Bidding（出价）等广告核心算法原理，有主导过核心模块端到端优化的经验，具备架构级设计能力。
精通 Two-Tower、Multi-Task Learning (MTL)、MMOE、Deep Interest Network (DIN)、SIM 等推荐/广告经典模型架构，并有改进或提出新架构的经验。
具备强化学习（RL）在广告出价与预算分配中的深度实践经验，有 Offline RL、Contextual Bandit 或 Model-based RL 的实际落地案例。
- 有大规模分布式 ML 系统设计和优化经验（数据规模 TB+，模型参数量亿级+）。
