This role offers the opportunity to work on services where every decision matters – from architectural choices to caching strategies to error handling – because our services are in the critical path for customer experience during the biggest moments in sports.
Key job responsibilities:
– Build architecture and feature development for our Tier-1 service.
– Build and maintain highly resilient distributed systems that handle millions of requests during peak live sports events.
– Design and implement sophisticated content curation and filtering logic that integrates with multiple upstream services for entitlements, blackouts, recommendations, and playability.
– Partner with other engineers to ideate, design, and build scalable solutions.
– Tackle complex technical challenges across the stack – from service APIs to data orchestration.
As a member of our team, you will:
– Own and operate mission-critical, Tier-1 services with stringent availability and performance requirements.
– Build and scale high-throughput, low-latency content discovery services deployed across multiple AWS regions globally.
– Drive operational excellence for services during peak live sports events.
– Architect solutions that integrate with multiple upstream services for content filtering, entitlements, blackouts, and recommendations.
– Work on intelligent content curation systems that surface the right live sports content to the right customers at the right time.
– Collaborate with merchandising, recommendation, and content teams to deliver personalized sports discovery experiences.
Basic Qualifications:
– Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design.
– Experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems.
Preferred Qualifications:
– Experience leading and influencing your team or organization, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution.
