Staff Machine Learning Engineer

What You Will Do:

• Collaborate closely with Data Scientists and Data Analysts to translate experimental code into production-ready architectures, driving code refactoring, model hosting decisions, and seamless integration.
• Design and formalize end-to-end model deployment processes, establishing standardized workflows for model versioning management to ensure reproducibility and reliability in production.
• Build and maintain robust data pipelines leveraging modern big data and distributed processing ecosystems, while establishing standardized Data Science development environments to boost cross-functional team productivity.
• Implement foundational ML infrastructure and standards, including feature store integration and data versioning management, tailored to support scalable business growth.
• Develop and operationalize predictive solutions using appropriate statistical, analytical, or heuristic approaches, when business needs require, bridging analytical insights with operational execution.
• Monitor, maintain, and optimize existing deployed solutions, proactively identifying bottlenecks, addressing technical debt, and ensuring high availability of production models.

Basic Qualifications

• Bachelor's/Master's degree in computer science, software engineering, industrial engineering, or any other quantitative disciplines, with 5+ years of relevant software/ML engineering experience in industry
• Hands-on experience in MLOps and model deployment lifecycle, including code refactoring, containerization (Docker, Kubernetes), and model hosting strategies (e.g., FastAPI, REST APIs, or cloud-native endpoints)
• Solid proficiency in Python, SQL, and distributed data processing frameworks (e.g., Spark, Hive, or equivalent big data technologies), with proven experience in building and maintaining scalable data pipelines
• Practical experience in setting up and standardizing ML foundations, such as feature stores, data versioning tools (e.g., DVC), and model registry/versioning management systems
• Strong software engineering best practices, including version control (Git), CI/CD pipelines, code testing, and clean architecture design
• Excellent written and verbal communication skills to effectively collaborate with Data Scientists, product managers, and software engineering stakeholders

Preferred Qualifications:

• Demonstrated experience working side-by-side with Data Scientists in an applied research or advanced analytics environment
• Familiarity with building and deploying lightweight or moderate-complexity machine learning models (e.g., forecasting, classification, or regression tasks)
• Experience evaluating and implementing internal tooling and lightweight frameworks to streamline DS workflows and accelerate time-to-market
• A deep understanding of the e-commerce or supply chain domain, with a track record of driving scalable engineering solutions that deliver measurable business impact

Recruitment Process

• Application Review - Phone Interview - Onsite (or Virtual Onsite) Interview – Offer
• The exact nature of the recruitment process may vary according to the specific job and may be changed due to scheduling or other circumstances.
• Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage.

Details to Consider

• This job posting may be closed prior to the stated end date for application if all openings are filled.
• Coupang has the right to rescind an offer of employment if a candidate is found to have submitted false information as part of the application process.
• Coupang does not discriminate against disabled applicants or those with veteran status.
• We are proud to offer equal opportunities for all applicants.

Privacy Notice

• Your personal information will be collected and managed by Coupang as stated in the Application Privacy Notice is located below. https://privacy.coupang.com/en/land/jobs/