EdgeLink as part of Amazon Traffic Engineering manages internet ingress for Amazon retail and non-retail customer experiences. Our mission is to safeguard Amazon's digital presence through world-class internet ingress management and security measures. We strive to enhance customer experiences by providing responsive, secure, and resilient content delivery networks and edge computing solutions. By offering comprehensive services for deploying, operating, and securing our global infrastructure, we ensure uninterrupted service availability and empower Amazon to deliver exceptional value to its customers worldwide.
We are building the next-generation real-time streaming analytics platform to understand and safeguard Amazon’s global traffic. Processing billions of requests and petabytes of data daily, our systems power observability, security, and ML-driven anomaly detection. Our work is foundational to the availability, performance, and trustworthiness of Amazon’s customer-facing products.
If you’re passionate about solving complex problems at scale and driving architectural decisions in a high-impact domain, you’ll feel right at home.
The ideal candidate brings deep technical expertise—including in machine learning—strong leadership skills, and a proven ability to drive cross-organizational alignment. Experience with applying ML techniques to streaming data, anomaly detection, traffic modeling, or predictive analytics is highly valued. You are a champion of engineering excellence, continuously raising the bar on quality, maintainability, performance, and resilience of large-scale data platforms. You’ll help shape the product vision, define long-term strategy, and drive outcomes that make a global impact.
There are no limits to the contribution and influence this role offers — come make history with us.
Key job responsibilities
Lead the design and development of scalable, real-time streaming systems that process high-throughput traffic data.
Architect and implement data pipelines and analytics frameworks that enable ML-based anomaly detection and traffic intelligence.
Partner with scientists and ML engineers to productionize models and build feedback loops for continuous learning.
Drive technical direction, make key architectural decisions, and own end-to-end solutions from concept to production.
Set best practices for coding, testing, deployment, and operations of distributed systems.
Mentor and guide junior engineers, conduct design reviews, and foster a high bar for technical excellence.
Collaborate cross-functionally with infrastructure, security, and product teams to influence roadmap and priorities.
