WSC | .NET Backend Developer
Senior Backend Engineer - AI Indexing
We turn a live sports broadcast into personalized, ready-to-publish highlights while the game is still going, automatically and at a scale no human editing team could match.
We're looking for a senior backend engineer to join the team that owns the Indexing layer:
the video-analysis pipeline and tagging engine that determine what happened in a game and what's worth showing. It runs high-performance compute workloads on Kubernetes and spans 30+ backend microservices in an event-driven orchestration system,runs 24/7 with elastic scaling that can grow to very high capacity. Problems in Indexing surface immediately in every downstream product.
What you'll do
Design, build, and operate backend microservices and APIs at scale, with reliability, latency, cost, and operability as first-class concerns.
Own projects end to end: clarify the requirement, propose the approach, ship incrementally, then follow through on rollout, monitoring, and iteration.
Work with Data Scientists and ML Engineers to get models into production — deployment, inference, monitoring — and design the surrounding systems to handle model failure and drift.
Build event-driven flows on pub/sub infrastructure (Azure Service Bus, Kafka).
Investigate throughput, latency, and stability problems under spiky peak traffic, and improve observability and runbooks off the back of what you find.
Requirements:
Bachelor's degree in Computer Science, Software Engineering, or a related field—or equivalent practical depth.
5+ years building, scaling, and operating backend systems in production cloud environments.
A track record of getting up to speed in a large system you didn't build and delivering real work early, without needing it handed to you in pieces.
Strong in at least one mainstream backend language. We're a C#/.NET shop, but strong Java, Python, or Go engineers are welcome.
Real experience with distributed systems and microservices, including Kubernetes workloads.
Hands-on work with AI coding tools such as Claude Code or Cursor, with judgment about where they help and where they don't, working with skills and sub agents.
Production experience with pub/sub and messaging systems.
SQL, relational schema design, and performance-minded querying.
A methodical approach to hard problems: you decompose, prioritize, and can explain your reasoning to people who don't share your context.
Willingness to own a service after it ships, and to change your position in a design review when someone makes a better argument.
Bonus:
A working understanding of how AI and ML systems behave in production — inference, context limits, evaluation, non-deterministic output — and what that means for the services around them.
Azure at scale, including networking, identity, and cost/performance tradeoffs.
Experience in video, media, or ML-heavy pipelines.
