Senior Staff Machine Learning Engineer-BJ/SH
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.
As a Senior Staff Machine Learning Engineer in Search and Discovery, you will leverage the most advanced algorithms to improve Coupang’s product search and discovery quality. You will spearhead the integration of Large Language Models (LLMs) and Generative AI to revolutionize our core search pipeline—including query understanding, product understanding, semantic retrieval, and final-stage ranking. You are expected to be a leading expert in one or multiple areas including information retrieval, recommendation systems, machine learning, deep learning, or natural language processing. The ideal candidates are pragmatic, resourceful, embrace seemingly impossible challenges, and have a proven track record of deploying high-scale, low-latency AI solutions in production.
What You Will Do
- System Diagnostics: Root cause the bottlenecks of the current search and recommendation systems and quantify the defects across the entire search funnel (query understanding $\rightarrow$ retrieval $\rightarrow$ ranking).
- State-of-the-Art Solutions: Propose and architect end-to-end solutions that leverage advanced ML, DL, and Generative AI/LLMs to upgrade traditional search components into AI-native architectures.
- Core Funnel Optimization with LLMs:
- Query Understanding: Build next-generation query understanding services (intent detection, query rewrite, semantic expansion, entity extraction) utilizing fine-tuned LLMs.
- Candidate Retrieval: Design and scale semantic/dense retrieval systems, combining traditional vector search (ANN) with generative retrieval techniques.
- Ranking & Reranking: Develop and optimize LLM-based listwise/pairwise reranking models, and tackle performance trade-offs (latency, cost, throughput) for real-time serving.
- Production & Evaluation: Implement solutions, optimize offline/online inference performance (using model distillation, quantization, vLLM/TensorRT), and conduct rigorous A/B tests.
- Technical Leadership: Be a thought leader, mentor senior engineers, and gain consensus across cross-functional organizations (Infrastructure, Platform, Product) to drive the search AI roadmap.
Example Projects Include
- LLM-Powered Query Understanding: We train and fine-tune in-house LLMs to perform multi-task query tagging, real-time query rewriting/expansion, and precise intent categorization.
- Generative & Semantic Retrieval: We use deep representation learning, two-tower models, and LLM-generated semantic embeddings combined with vector databases (e.g., Milvus, Vespa) for hyper-accurate candidate retrieval.
- LLM Reranking & Distillation: We explore listwise and pairwise LLM reranking models to evaluate query-item relevance, while distilling their reasoning capabilities into smaller, sub-millisecond models for real-time serving.
- Deep Conversion & Personalization: We build Deep Learning-based conversion prediction models ($CVR$) incorporating real-time user state, query intent, and multi-modal product representations.
- Graph-based Relationships: We leverage graph neural networks (GNNs) and knowledge graphs to ground our LLMs, exploring latent relationships between query sessions and catalog documents.
Basic Qualifications
- 10+ years of professional experience in related fields including Search, Information Retrieval, RecSys, ML, DL, or NLP.
- Solid software engineering foundation with proficient coding skills in Python, C++, or Java.
- Distributed Systems Experience: Proven experience handling large data volumes, training large-scale models, and serving high-QPS pipelines in distributed environments (e.g., Spark, Ray, Kubernetes).
- Pragmatic Problem Solver: Demonstrated ability to take ambiguous business problems, formulate them mathematically, and deliver production-grade ML solutions.
- Education: Master’s Degree in Computer Science, Data Science, or related engineering fields.
Preferred Qualifications
- LLM & GenAI Expertise: Deep hands-on experience in fine-tuning (SFT, LoRA), aligning (RLHF/DPO), or distilling LLMs specifically for search, recommendation, or NLP tasks (e.g., query expansion, dense retrieval, reranking).
