Senior Staff, Machine Learning Engineer (Coupang Eats Search)
Lead ML innovations for Coupang Eats Search, shaping how customers discover food/delivery items via retrieval and relevance models. Own end-to-end stack, including query understanding, candidate retrieval, and relevance measurement, while driving business growth through scalable, low-latency solutions.
- Compensation
- Not disclosed
- Location
- Seoul, South Korea
- Work setup
- Not specified
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 10+ years in search, information retrieval, NLP
- Deep expertise in product understanding, retrieval, relevance, and modern LLM techniques
- Proven delivery of large-scale consumer search systems with measurable customer and business impact
- Experience in designing retrieval methods for distributed, low-latency systems at scale with relevance guarantee
- S. or Ph.D. in Computer Science or related technical fields
Nice to have
- Food delivery, local commerce, e-commerce, or advertising experience
- Hyperlocal, time-sensitive ranking using location, availability, delivery time, price, and promotion signals
- Multilingual or localized search experience across markets
- Practical experience with embeddings, transformers, LLMs that delivered measurable impact
- Publications in search, NLP, e-commerce, or advertising
Full description
**About us
**Coupang Eats is creating new experiences that make everyday life more convenient for customers by expanding its services beyond food delivery into grocery and retail shopping. Driven by a constant commitment to delivering a better customer experience, we continue to challenge ourselves and lead innovation across new services and categories.
Coupang Eats empowers employees who create opportunities and deliver results by giving them greater roles and responsibilities. Here, you can turn your ideas into action, create even greater opportunities through your achievements, and experience the fulfillment of directly driving business growth.
If you want to grow alongside experts in their respective fields and take on even greater challenges together, join Coupang Eats today.
**About the Role
**Lead ML innovations on Retrieval and Relevance for Coupang Eats Search, one of Asia’s fastest-growing commerce businesses. You will shape how customers discover brands, stores, dishes, and products across food delivery and quick commerce, owning the machine learning models and strategies behind retrieval and relevance. As the ML Lead for Coupang Eats Search, you will be accountable for increasing successful retrieval of brands, stores, dishes, and products, and for driving measurable improvements in relevance and conversion. You will own the end-to-end stack, including query and menu understanding, candidate retrieval, relevance, and quality evaluation. You will establish a multi-year roadmap and lead projects across machine learning and engineering. Food-delivery search is dynamic, hyperlocal, and time-sensitive. Results must reflect customer intent while adapting to location, restaurant and menu availability, delivery time, price, promotions, quality, and real-time customer context. The ideal candidate combines deep search and NLP expertise with strong product judgment and organizational leadership, and can balance quality, latency, reliability, and experimentation velocity.
What You Will Do (Key Responsibilities)
- Customer and Business Outcomes: Own the machine learning strategy, metrics, and execution for improving brand, store, dish, and product retrieval and increasing conversion across Coupang Eats Search surfaces. Establish clear outcome metrics and targets and use rigorous experimentation and diagnostics to deliver sustained improvements while protecting key guardrail metrics and customer experience.
- Query and Product Understanding: improve query intent and entity understanding, menu and restaurant representation, typo tolerance, synonym expansion, and query rewriting.
- Retrieval: Advance lexical, semantic, and hybrid retrieval. design scalable, low-latency retrieval strategies, including indexing, feature pipelines, model life cycle.