Staff Engineer

We are looking for a top-tier Staff Engineer with deep expertise in designing, building, and scaling fraud detection and risk prevention systems for high-traffic, production-grade products. The ideal candidate will have strong hands-on engineering capabilities, experience architecting complex distributed systems, and a proven track record of building fraud detection capabilities that operate at significant scale. This role requires an engineer who can translate complex fraud patterns and risk signals into reliable, real-time detection systems while maintaining high performance, accuracy, and scalability. We are looking for someone who has owned critical engineering initiatives end-to-end, from system design and architecture to implementation, deployment, and continuous improvement in production.

Responsibilities:

• Design, architect, and build scalable fraud detection, risk scoring, and anomaly detection systems for high-volume products.
• Develop real-time and near-real-time systems to identify, evaluate, and prevent fraudulent transactions, activities, accounts, or behavioral patterns.
• Architect distributed, fault-tolerant systems capable of processing large volumes of events and making low-latency decisions.
• Build and improve fraud detection engines using rules-based systems, behavioral analytics, anomaly detection, risk signals, and machine learning where applicable.
• Design event-driven architectures and data pipelines to process high-volume transactional and behavioral data.
• Establish robust system design practices covering reliability, scalability, latency, fault tolerance, and operational resilience.
• Investigate sophisticated fraud patterns and evolving attack vectors, translating them into effective technical solutions.
• Drive engineering initiatives across teams, influence architectural decisions, and establish technical standards.
• Monitor production performance and continuously improve detection effectiveness, system reliability, and operational efficiency.

Requirements:

• 8+ years of strong software engineering experience, with demonstrated technical leadership at a staff engineer or equivalent scope.
• Proven hands-on experience designing and building fraud detection, fraud prevention, risk engineering, or closely related abuse detection systems.
• Strong system design and distributed systems expertise, with experience building high-scale, production-grade platforms.
• Demonstrated ownership of critical systems from architecture and development through deployment and production operations.
• Experience with real-time event processing, high-throughput data pipelines, streaming architectures, or low-latency decision systems.
• Strong programming skills in languages such as Java, Go, Python, C++, or similar.
• Experience handling large volumes of transactions, events, or behavioral data with demanding latency and reliability requirements.
• Strong understanding of production engineering, observability, performance optimization, and fault-tolerant system design.
• An engineer who has personally designed and built fraud detection systems, rather than merely supported or maintained existing platforms.
• Strong architectural depth and the ability to make complex engineering decisions independently.
• Experience solving fraud and risk problems at a significant production scale.
• Evidence of measurable impact, such as improved fraud capture rates, reduced false positives, lower detection latency, or increased transaction-processing capacity.
• Staff-level technical ownership, combining hands-on coding with architecture.

Preferred Qualifications:

• Experience with technologies such as Kafka, Flink, Spark, distributed databases, and stream-processing frameworks.
• Exposure to machine learning-based fraud detection, anomaly detection, risk scoring, or graph-based fraud analysis.
• Experience in fintech, payments, banking, e-commerce, marketplaces, digital platforms, or other products exposed to sophisticated fraud risks.
• Experience designing systems that balance fraud detection accuracy, false-positive rates, customer experience, and operational cost.
• Track record of leading complex, cross-functional technical initiatives and mentoring senior engineers.