Staff Machine Learning Engineer (MLOps)

Location: Taipei, Taiwan

Company

An e-commerce and logistics platform focused on fast delivery and international product access in Taiwan.

What you will do

• Translate experimental data science code into production-ready architectures through refactoring, model hosting, and system integration.
• Design end-to-end model deployment processes with standardized versioning, reproducibility, and reliability practices.
• Build and maintain scalable data pipelines using distributed processing technologies.
• Establish ML infrastructure, including feature stores, data versioning, model registries, and standardized data science environments.
• Develop and operationalize predictive solutions using statistical, analytical, and heuristic approaches.
• Monitor and optimize deployed models, addressing bottlenecks, technical debt, and availability issues.

Requirements

• Bachelor’s or master’s degree in computer science, software engineering, industrial engineering, or another quantitative discipline.
• 5+ years of relevant software or machine learning engineering experience in industry.
• Hands-on MLOps and model deployment experience, including Docker, Kubernetes, FastAPI, REST APIs, or cloud-native endpoints.
• Strong Python, SQL, and distributed data processing experience with Spark, Hive, or equivalent technologies.
• Experience with feature stores, data versioning, model registries, Git, CI/CD, testing, and clean architecture.
• Strong communication skills for collaboration with data scientists, product managers, and software engineering stakeholders.

Nice to have

• Experience collaborating with data scientists in applied research or advanced analytics environments.
• Experience building forecasting, classification, or regression models.
• Experience developing internal tooling and lightweight frameworks for data science workflows.
• Deep understanding of e-commerce or supply chain domains.

Culture & Benefits

• Opportunity to contribute to the expansion of an e-commerce service in Taiwan.
• Work involves close collaboration across data science, product management, and software engineering.
• Equal employment opportunities are provided, including for disabled applicants and veterans.

Hiring process

• Application review followed by a phone interview.
• Onsite or virtual onsite interview.
• Offer after the interview process; scheduling and process details may vary.