Senior Staff/Staff Machine Learning Engineer

Coupang
Beijing, ChinaPosted 26 March 2026

Job Description

Senior Staff/Staff Machine Learning Engineer-LLM 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 sped we have been at since our inception. We are all entrepreneurial 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. Summary: At Coupang, we invest in providing the best product experience for our customers. The AI and Knowledge Engineering team’s mission is to provide the best possible technical solution to achieve the optimal experience for our customers. We innovate and advance the space of Knowledge Graphs, agents, LLMs, AI, ML, NLP, and multi-modal models. We are looking for engineers who want to work on cutting edge technology, that is the intersection of Semantic knowledge bases, agents, NLP, LLM, ML and software engineering. Come join us to help create a world class team advancing the AI/NLP/ML/LLM/RL/Knowledge Graph technologies. The Role/Responsibilities: Work on GenAI, self-learning systems to solve complex eCommerce scenarios. Develop statistical models for predictive and compound AI Models Train foundational models that can help with building learning systems. Define process and experiments for an E2E foundation model. Build E2E AI pipelines for storing/servicing foundational models. Mentor and guide team members on frameworks, methodologies related to data lakes, data normalization, dimension reduction, visualization, statistical models. Ensure Model training is security, compliance aware. Collaborate with cross-team members to understand the requirements ensuring the foundational models, knowledge captured aligns with the company’s overall AI strategy. What you will bring/ Requirements: Overall 10+ years of working in software engineering and AI/ML Engineering. 4+ years of demonstrated data science, experience in building large scale foundational models for eCommerce scenarios. Expertise in large language model fine-tuning and deployment. Deep understanding of AI/ML/Deep-learning and statistical model architecture. Strong statistical/mathematical background in defining metrics, learning functions and result analysis. Significant experience in SQL, Python, PySpark, Scala and R Proficiency in PyTorch/TensorFlow, Spark, BigQuery Experience in creating complex data visualization tools, frameworks and techniques. Experience in dimension reduction in big data. Proficient in cloud analytical stacks [ Lakes, warehouse, ETL pipelines] Experience with inference platforms in AWS, Azure and other open-source platforms. Are result-oriented, with a bias towards customer obsession, flexibility, and impact. Strong written and verbal communication and teamwork skills Passion for Large language models, NLP, and Machine Learning at scale Education: B.S, M.S in Computer science or Mathematics, or related field Recruitment Process Application Review - Phone Interview - Onsite (or Virtual Onsite) Interview – Offer The exact nature of the recruitment process may vary accordin ... (truncated, view full listing at source)
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