Staff Machine Learning Engineer

Coupang
Mountain View, USAPosted 1 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 As our Staff Machine Learning Engineer for Coupang Media Group, Ads Quality, you will be responsible for developing, designing machine learning models, optimization algorithms and new product features for our advertising platform. The Coupang Media Group (CMG) is responsible for providing vendors that sell their products on Coupang a host of marketing products and services.  We launched Product Ads performance advertising marketplace late in 2018.  We have grown rapidly to over 10,000 active advertisers and 150M daily ad impressions in only 5 months’ time. Our group is responsible for advertising brands and products inside our e-commerce website. To get an idea of how valuable that is, both Alibaba and Amazon make more money on advertising than on selling products. This is an opportunity to be part of building up a new development organization with strong revenue potential inside a successful company. We’re building full-stack engineering teams to handle large scale advertising problems, including real-time behavioral targeting, auctioning and bidding systems and search based advertising. We are in the growth phase of the organization, so you’ll be a part of managing the transition from growth phase to enterprise processes. In this role you will be responsible for innovating and building our new ad quality stack for our ads platform  and eventually extend it into a full ad exchange, DSP, and SSP. We currently handle about 1bn impressions per day from internal traffic alone. 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, statistic ... (truncated, view full listing at source)
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