4 hours ago
Taipei, TaiwanStaff+
Responsibilities
- Collaborate with Data Scientists and Data Analysts to refactor experimental code into production-ready architectures and select model hosting strategies.
- Design end-to-end model deployment processes and standardized model versioning workflows for reproducible and reliable production systems.
- Build and maintain scalable data pipelines using big data and distributed processing technologies.
- Establish standardized Data Science development environments and foundational ML infrastructure, including feature stores and data versioning.
- Develop and operationalize predictive solutions using statistical, analytical, or heuristic approaches.
- Monitor, maintain, and optimize deployed solutions by addressing bottlenecks, technical debt, and production availability.
Requirements
- Bachelor’s or master’s degree in computer science, software engineering, industrial engineering, or another quantitative discipline.
- 5+ years of relevant industry experience in software or ML engineering.
- Hands-on MLOps and model deployment lifecycle experience, including code refactoring, Docker, Kubernetes, and model hosting strategies such as FastAPI, REST APIs, or cloud-native endpoints.
- Proficiency in Python, SQL, and distributed data processing frameworks such as Spark or Hive, with experience building scalable data pipelines.
- Experience with feature stores, data versioning tools such as DVC, and model registry or versioning systems.
- Strong software engineering practices involving Git, CI/CD pipelines, code testing, and clean architecture design.
- Strong written and verbal communication skills for collaboration with Data Scientists, product managers, and software engineering stakeholders.
- Preferred: experience working with Data Scientists in applied research or advanced analytics environments.
- Preferred: experience deploying lightweight or moderately complex forecasting, classification, or regression models.
- Preferred: experience evaluating internal tooling and lightweight frameworks for Data Science workflows.
- Preferred: e-commerce or supply chain domain knowledge and a record of delivering scalable engineering solutions with measurable business impact.
Tech Stack
Categories
Data EngineeringML Engineering
About Coupang
Coupang builds and operates a South Korea–focused e-commerce marketplace with an end-to-end logistics network (Rocket Delivery), plus food delivery, video streaming, and fintech under brands such as Coupang, Eats, and Play. Revenue comes from first-party retail, third-party marketplace services, advertising, and memberships (Rocket WOW). Founded in 2010, the company is headquartered in Seattle and is publicly listed on the NYSE (CPNG).