Staff Machine Learning Engineer

Please complete the attached the Internal Transfer Request Form and submit it.  
Please make sure you are applying with your Coupang e-mail address. 

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. 

Role Overview

At Coupang, we invest in providing the best product experience for our customers. The AI and Knowledge Engineering team’s mission is to provide the best possible technical solution to achieve the optimal experience for our customers. We innovate and advance the space of Knowledge Graphs, agents, LLMs, AI, ML, NLP, and multi-modal models. We are looking for engineers who want to work on cutting edge technology, that is the intersection of Semantic knowledge bases, agents, NLP, LLM, ML and software engineering.

Come join us to help create a world class team advancing the AI/NLP/ML/LLM/RL/Knowledge Graph technologies.

Basic Qualifications

• Architect and design large scale inference, recommendation and interactive agents’ system that are self-learning
• Design/architect/work on large language model-based agent systems
• Develop and customize/tune deep learning generative models, deep learning models for multi-modal for eCommerce customer scenarios
• Train foundational models that can help with building learning systems
• Define process and experiments for an E2E foundation model
• Refine agent and model training using RAG
• Build E2E AI pipelines for storing/servicing foundational models
• Mentor and guide team members on frameworks, methodologies related to LLMs, RAGs and their limitations, semantic web, reinforcement learning
• Ensure Model training is security, compliance aware
• Collaborate with cross-team members to understand the requirements ensuring the foundational models, knowledge captured aligns with the company’s overall AI strategy

Preferred Skills

• Overall 10+ years of working in software engineering and AI/ML Engineering
• 3+ years of demonstrated experience in building large scale foundational models, models for eCommerce scenarios.
• Expertise in large language model fine-tuning and deployment.
• Deep background in transformer architectures
• Understand RAG and its limitations for multi-modal knowledge retrieval.
• Proficiency in Python, PyTorch/TensorFlow, Spark, BigQuery
• Experience with inference platforms in AWS, Azure and other open-source platforms.
• Are result-oriented, with a bias towards customer obsession, flexibility, and impact.
• Strong written and verbal communication and teamwork skills
• Passion for Large language models, NLP, and Machine Learning at scale
• Education: B.S, M.S or PhD in Computer science, or related field

 Please complete the attached the Internal Transfer Request Form and submit it.  
Please make sure you are applying with your Coupang e-mail address. 

Coupang is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to actual or perceived race (including traits historically associated with race, including but not limited to hair texture and protective hair s