Sr. Staff, Machine Learning Engineer [L7-1] (Search & Discovery)

The role

You will drive the design, development, and evolution of end-to-end recommendation systems. This involves architecting ranking, retrieval, and relevance models and scaling robust systems. You will partner with global cross-functional teams to elevate the user experience through AI/ML innovation.

What you will do

• Own the Recommendation service.
• Collaborate with multiple global teams to co-build a recommendation system.
• Mine vast amounts of data to gain insights from customer behavior.
• Develop new Machine Learning models and features.
• Create data jobs to generate Machine Learning features.
• Build and train new models.
• Run experiments to validate and launch new models.
• Mentor and grow engineers across the teams.

Required qualifications

• Bachelor’s degree in Computer Science, Electrical Engineering, Mathematics, Statistics or closely related fields.
• Ability to derive actionable insights from complex datasets and solve business-critical problems using advanced AI/ML technologies.
• Experience as a company-wide technical leader driving cross-functional collaboration.
• 8+ years of professional experience in applied AI/ML with large-scale, complex datasets.
• Track record of launching successful algorithms that drive measurable business impact.
• Professional proficiency in Python and/or Java with relevant industry experience.
• Professional proficiency in English.

Nice to have

• Ph.D. or Master degree in Computer Science, Electrical Engineering, Mathematics, Statistics or related science majors
• Experience with Recommendation or Search Systems
• Experience with LLMs, PLMs, and embedding models
• Experience with cloud platforms such as AWS, GCP including services like Vertex AI
• Industrial experience working with online serving system working at scale
• Great listening skills, growth mindset and can-do attitude

About the company

Coupang aims to wow customers by making shopping, eating, and living easier. The company has a startup culture with the resources of a large global public company.