Staff~Sr. Staff Machine Learning Engineer (AI Agent)

Company Introduction We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did we 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. About the Role Full-Lifecycle LLM Fine-Tuning: Deeply participate in the full-stack algorithm RD for Coupang's next-generation AI shopping assistant and intelligent customer service products. Lead the full-lifecycle tuning of Large Language Models (LLMs), including Continual Pre-Training (CPT), Supervised Fine-Tuning (SFT), and Reinforcement Learning from Human/AI Feedback (RLHF / Agentic RL). Large-Scale Agent System Construction: Design and optimize large-scale Agent Orchestration frameworks. Implement tool-calling and sub-agent collaboration mechanisms, empowering the AI assistant to complete the end-to-end shopping loop—spanning pre-sales consultation, price comparison, purchasing, and after-sales service. Data Flywheel Feedback Loop: Leverage real user feedback data to build a high signal-to-noise ratio data flywheel and reinforcement learning feedback loop. Continuously drive model iterations to directly boost the user experience and core business metrics. Generative Multi-Modal Product Discovery: Integrate multi-modal LLM technologies to reinvent traditional search and recommendation pipelines. Build a generative AI-based multi-modal recall, rough-ranking, and fine-ranking system to establish a next-generation, business-goal-oriented intelligent product discovery mechanism. Job Requirements: Master's degree or above in Computer Science, Artificial Intelligence, Machine Learning, or a related field (Ph.D. is highly preferred). Solid practical experience in LLM training and tuning (Pre-training / SFT / RL). Familiar with mainstream deep learning frameworks (e.g., PyTorch) and distributed training technologies. Deep domain knowledge and a proven track record of implementing solutions in core e-commerce areas, such as intelligent customer service, AI search, AI shopping assistants, or Generative Recommendation. In-depth understanding and rich project experience in algorithm tuning for Agent architectures, Tool-use, and Multi-Agent Systems. Practical experience in user personalization modeling, long-text/ultra-long context understanding, and LLM Context Memory management. Passionate about "reshaping the e-commerce shopping experience with AI." Candidates with high-quality papers published in top-tier AI conferences (e.g., ICML, ACL, AAAI, NeurIPS, ICLR, CVPR) or holding relevant technical patents will be given priority. What We Offer (The Coupang Advantage): Massive 0-to-1 Business Impact: Lead the creation of a next-generation AI shopping assistant benchmarking against industry leaders (like Rufus). Your models and Agent designs will directly serve tens of millions of global users and fundamentally change how they shop. Abundant Compute Cutting-Edge Research: Backed by abundant GPU cluster computing power, the t ... (truncated, view full listing at source)