Sr. Software Dev Engineer, Verification Services
Listed on Amazon’s official careers site. Posted October 12, 2026. Check that it is still open and apply there.
Apply on Amazon’s careers site →Job description
Amazon's Seller Identity & Verification team operates at the core of our marketplace security infrastructure. We build and maintain high-scale verification systems that ensure marketplace compliance and seller authenticity across Amazon's global marketplace.
Our engineering teams focus on three key areas
building streamlined, globally distributed registration systems; implementing robust identity verification services with real-time validation; and developing automation frameworks that maintain high accuracy at scale. We solve complex challenges around distributed state management, security protocols, and data consistency across regions.
We are growing our team to simplify seller onboarding and enable global operations - building modern cloud-native services, event-driven architectures, and scalable data models that support multi-region expansion.
About the Role
We are seeking Senior Engineers with deep experience in distributed systems design and implementation. You will build high-throughput, low-latency services using modern cloud-native architectures. This is a hands-on technical leadership role where your architectural decisions directly impact Amazon's global marketplace operations.
Key job responsibilities
- Design and implement globally distributed systems with emphasis on scalability, reliability, and fault tolerance
- Drive cross-regional data consistency, caching strategies, and distributed transaction management
- Build and own high-availability services that handle complex state management across multiple regions
- Lead technical initiatives end-to-end, from architecture through delivery
- Mentor engineers and drive engineering excellence across the team
- Collaborate with cross-functional teams to define technical direction and solve ambiguous problems
- Establish and maintain CI/CD pipelines and DevOps best practices for large-scale systems
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