Staff Machine Learning Engineer

About the role

Coupang is expanding in Taiwan and is looking for a Staff Machine Learning Engineer to bridge the gap between data science and production systems. You will work closely with Data Scientists to bring predictive models to life, set up deployment pipelines and foundational infrastructure, and drive MLOps best practices. The role suits someone who enjoys laying technical foundations from scratch, with scalability, maintainability and high performance.

Key responsibilities

• Work with Data Scientists and Data Analysts to turn experimental code into production-ready architectures, covering code refactoring, model hosting decisions and integration.
• Design and formalize end-to-end model deployment processes, including standardized workflows for model versioning.
• Build and maintain data pipelines using modern big data and distributed processing ecosystems, and set up standardized Data Science development environments.
• Implement foundational ML infrastructure and standards, including feature store integration and data versioning.
• Develop and run predictive solutions using statistical, analytical or heuristic approaches when needed.
• Monitor, maintain and optimize deployed solutions, fix technical debt and keep production models highly available.

Required profile

• Bachelor's or Master's degree in computer science, software engineering, industrial engineering or another quantitative discipline, with 5+ years of software/ML engineering experience.
• Hands-on MLOps and model deployment experience, including code refactoring, containerization and model hosting strategies.
• Experience setting up and standardizing ML foundations such as feature stores, data versioning tools and model registries.
• Strong software engineering practices: version control, CI/CD pipelines, code testing and clean architecture.
• Good written and verbal communication with Data Scientists, product managers and engineering stakeholders.

Required skills

• Python and SQL
• Distributed data processing frameworks such as Spark or Hive
• Containerization (Docker, Kubernetes)
• Model hosting (FastAPI, REST APIs, cloud-native endpoints)
• Data versioning tools such as DVC

Preferred: hands-on work alongside Data Scientists, experience deploying lightweight ML models (forecasting, classification, regression), experience building internal tooling, and knowledge of e-commerce or supply chain.