Sr Staff Machine Learning Engineer

Coupang
Seattle, USAPosted 2 May 2026

Job Description

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’re collectively disrupting the multi-billion dollar e-commerce industry from the ground up. We are one of the fastest-growing e-commerce companies that established an unparalleled reputation for being a dominant 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. Role Overview The Search Discovery organization is responsible for optimizing customers' navigation experience and driving long-term growth. Every week millions of customers use search in our app or on coupang.com, more than one third of them make purchases. Over half of the Coupang sales are directly attributed to search and recommendation. Many business initiatives and growth strategies are driven or boosted by ranking and recommendation. This role is an opportunity to build scalable offline and online ML platforms to support ranking and recommendation, and business decision making. You will be handling billions of records of user interaction data, and petabytes of raw log files, and transforming them into data objects that can be easily consumed by engineers and scientists. You will also be responsible for serving complex ML models in real time to help millions of Coupang customers search and discover products. If you are passionate about big data engineering and scalable serving systems, you want to make real business impact, and you are a true problem solver, this is the right job for you. What You Will do Design features and build large-scale machine learning models and systems to improve ad targeting, relevance, ranking, and engagement Design and implement large-scale ML systems for search ranking, semantic retrieval, query understanding, and personalized product discovery using state-of-the-art techniques such as transformer-based models, contrastive learning, and vector search Drive innovation in search relevance and user intent modeling using large language models (LLMs), embedding-based retrieval, and multi-modal learning Build and optimize ML pipelines using tools such as Apache Spark, Airflow, Kubeflow, and MLflow, ensuring reproducibility, scalability, and operational excellence Define and track key performance metrics to evaluate model impact and identify high[1]leverage opportunities for improvement Collaborate cross-functionally with product, engineering, and data science teams to align technical solutions with business goals and customer experience Mentor and grow engineering talent, fostering a culture of technical excellence, experimentation and continuous learning Basic Qualifications Bachelor's degree in computer science, electrical engineering, mathematics, statistics or closely related fields 4 years of professional experience in applied machine learning Experience in machine learning, deep learning, and statistical modeling Proficiency in Python and/or Java, with experience in building production grade ML systems Preferred Qualifications Master’s or PhD in relevant technical fields Experience with search systems, information retrieval or recommendation en ... (truncated, view full listing at source)
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