Staff Backend Engineer
Coupang · Bengaluru · 12+ yrs experience · Posted 2026-07-18
Tech stack: Java, Backend
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About the role
Responsibilities:
- The Commerce Risk Detection Systems (CRDs) team focuses on seller and buyer fraud detection and prevention across Coupang’s ecosystem.
- The team builds large-scale, real-time systems that analyze tens of millions of commerce events daily (orders, returns, listings) to identify fraudulent behavior and patterns.
- A Staff Backend Engineer will operate as a technical leader, driving architecture, scalability, and innovation in fraud detection systems.
- The team is actively evolving from rule-based systems toward ML-driven and LLM-powered detection mechanisms for future scalability.
- Act as a technical leader translating business fraud prevention goals into scalable technical solutions and architecture
- Design and build large-scale, low-latency backend systems processing tens of millions of events per day
- Develop and optimize real-time fraud detection systems using streaming data and inference pipelines
- Collaborate across stakeholders including Product, Data Science, ML engineers, and leadership
- Drive architectural decisions for scalable fraud detection (counterfeit detection, seller fraud, risk signals)
- Evaluate and implement ML and emerging LLM-based approaches for fraud detection use cases
- Advocate engineering excellence, including system efficiency, scalability, maintainability, and fault tolerance
- Guide best practices and act as a role model for strong engineering standards across the team
Qualifications:
- 10–12 years of backend engineering experience
- Strong experience building large-scale, real-time distributed systems
- Hands-on experience with data streaming technologies (e.g., Apache Flink or similar)
- Strong proficiency in Java-based backend development
- Experience designing and building low-latency, high-throughput systems
- Experience working with OLTP databases in production environments
- Experience building or supporting real-time inference systems (ML or rule-based)
- Familiarity with using GenAI/LLM tools for engineering productivity (e.g., coding, debugging, development workflows)
- Strong knowledge of system design, scalability, and fault tolerance
- Strong problem-solving skills with focus on efficiency and optimal solution
- Experience in fraud detection, risk systems, payments risk, or similar domains
- Familiarity with commerce or fintech ecosystems (e.g., payments, marketplaces)
- Experience with Apache Flink or similar streaming frameworks
- Experience with OLTP databases and backend system design
- Exposure to ML systems, feature stores, or inference pipelines
- Familiarity with leveraging GenAI tools (e.g., code generation, debugging, productivity enhancements)
- Exposure to using existing LLMs or AI assistants in engineering workflows (not model-building, but practical usage)
- Proficiency in Java-based backend development
Qualifications
- 10–12 years of backend engineering experience
- Strong experience building large-scale, real-time distributed systems
- Hands-on experience with data streaming technologies (e.g., Apache Flink or similar)
- Strong proficiency in Java-based backend development
- Experience designing and building low-latency, high-throughput systems
- Experience working with OLTP databases in production environments
- Experience building or supporting real-time inference systems (ML or rule-based)
- Familiarity with using GenAI/LLM tools for engineering productivity (e.g., coding, debugging, development workflows)
- Strong knowledge of system design, scalability, and fault tolerance
- Strong problem-solving skills with focus on efficiency and optimal solution
- Experience in fraud detection, risk systems, payments risk, or similar domains
- Familiarity with commerce or fintech ecosystems (e.g., payments, marketplaces)
- Experience with Apache Flink or similar streaming frameworks
- Experience with OLTP databases and backend system design
- Exposure to ML systems, feature stores, or inference pipelines
- Familiarity with leveraging GenAI tools (e.g., code generation, debugging, productivity enhancements)
- Exposure to using existing LLMs or AI assistants in engineering workflows (not model-building, but practical usage)
- Proficiency in Java-based backend development
Responsibilities
- The Commerce Risk Detection Systems (CRDs) team focuses on seller and buyer fraud detection and prevention across Coupang’s ecosystem.
- The team builds large-scale, real-time systems that
- analyze tens of millions of commerce events daily (orders, returns, listings) to identify fraudulent behavior and patterns.
- A Staff Backend Engineer will operate as a technical leader
- driving architecture, scalability, and innovation in fraud detection systems.
- The team is actively evolving from rule-based systems toward ML-driven and LLM-powered detection mechanisms for future scalability.
- Act as a technical leader translating business fraud prevention goals into scalable technical solutions and architecture
- Design and build large-scale, low-latency backend systems processing tens of millions of events per day
- Develop and optimize real-time fraud detection systems using streaming data and inference pipelines
- Collaborate across stakeholders including Product, Data Science, ML engineers, and leadership
- Drive architectural decisions for scalable fraud detection (counterfeit detection, seller fraud, risk signals)
- Evaluate and implement ML and emerging LLM-based approaches for fraud detection use cases
- Advocate engineering excellence, including system efficiency, scalability, maintainability, and fault tolerance
- Guide best practices and act as a role model for strong engineering standards across the team