Senior Staff Ranking Engineer

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 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. 

Job Overview

Search & Discovery is the most critical customer facing experience within Coupang, matching billions of products to customer intent. We are building the next-generation infrastructure to sustain the hypergrowth of our business for the coming years. To that end, we are looking for an experienced and passionate engineering leader who can drive this effort across the search stack including modification of serving and signal generation pipelines as well as driving improvements to our conversion prediction model which forms the backbone of search.The desired candidate can dive deep into underlying components, design and optimize the stack end-to-end and be a role model for building high-quality machine learning systems and delivering results with grit. Key to this position’s success is the ability to understand business needs and work backwards to develop technical solutions while working effectively with leaders of individual teams and aligning on technical direction with key stakeholders.This role will involve working closely with Search senior leadership (VP, Sr. Directors) and will offer the autonomy to identify and solve key Search problems and explore innovations in various areas like deep learning and large language modeling.Key Responsibilities:

• Build end-end Search & Discovery systems and help design the next-generation AI products to support the business growth
• Execute with the ability to drive key developments and overcome technical blockers
• Ensure solid ML design and operational excellence
• Be metrics driven and identify big opportunities
• Build strong collaborations with partner teams
• Recruit and develop world-class talent

Requirements:

• 10+ years of industry experience in technology companies.
• Extensive hands-on experience developing and training ML models in search, recommendations, and/or ads domains.
• Have a broad and deep understanding of key technology areas including information retrieval, deep learning, large language modeling, data processing, real-time serving.
• Solid track record of delivering large and complex systems with big business impacts.
• Strong design and problem-solving skills.
• Excellent communications and collaborations across teams and across functionalities.
• A passion for moving quickly to have measurable impact on a short time frame.
• A strong sense of ownership and proactive attitude towards solving challenging, open-ended problems.

Preferred Skills:

• Retrieval: Uses embedding-based retrieval (EBR) with Approximate Nearest Neighbor (ANN) search (e.g., FAISS, HNSW). Familiar with advanced techniques like contrastive learning or adaptive retrieval.
• Ranking: Know SOTA models like DCNv2, DeepFM, FFM and Generative Recommendation. Explores multi-objective loss functions (e.g., optimizing engagement, diversity, and revenue).
• Negative Sampling: Understands and applies standard in-batch and hard negatives to avoid overfitting to exposed distribution for retrieval and ranking.
• Model Evaluation: Discusses A/B testing, offline metrics (NDCG, MRR, precision@k), and online impact metrics (CTR, conversion rate).
• Cold Start & Exploration: Discusses meta-learning, few-shot learning, or reinforcement learning (multi-arm bandit and usage of randomization experiments).
• Scalability: Aware of serving constraints and may mention precomputing embeddings o