Staff Software Engineer, Infrastructure

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• →Set technical direction for core infrastructure systems, balancing reliability, scalability, security, and developer velocity.
• →Design and lead implementation of complex, cross-team infrastructure initiatives (AWS, EKS, CI/CD, shared platforms).
• →Build production-grade software to power infrastructure automation, tooling, and internal platforms.
• →Drive org-wide improvements in observability, incident response, reliability engineering, and cost efficiency.
• →Lead video infrastructure initiatives  to evolve video delivery and CDN strategies for global scale and performance.
• →Partner with AI and Data teams to define and build GPU-powered infrastructure, model-serving platforms, and GenAI inference/training workflows.
• →Act as a technical mentor and reviewer, raising the bar for infrastructure design and operational excellence across teams.
• →Lead by example during incidents, postmortems, and critical architectural decisions.

• 10+ years building and operating cloud-native production systems.
• Strong expertise in:
  • AWS architecture and large-scale cloud operations
  • Kubernetes / EKS (networking, RBAC, Helm, autoscaling, cluster-level debugging)
  • Infrastructure as Code (Terraform preferred)
  • CI/CD and automation (GitHub Actions, Argo, or similar)
• Proven ability to design and build infrastructure software, not just operate systems.
• Deep understanding of distributed systems, networking, Linux internals, and cloud security.
• A track record of driving step-function improvements in reliability, scalability, or developer experience.
• Strong written and verbal communication skills; able to influence technical decisions across teams.

• You think in systems and leverage, not just tasks.
• You care deeply about craftsmanship, clarity, and long-term maintainability.
• You enjoy solving ambiguous, cross-functional technical problems.
• You’re pragmatic—you optimize for impact over novelty.
• You raise the bar through mentorship, design reviews, and thoughtful collaboration.
• You’re energized by both strategic architecture and hands-on implementation.

• AI/ML infrastructure, GPU scheduling, or distributed training
• Model serving frameworks (e.g., Triton, vLLM, TensorRT-LLM)
• Infrastructure autoscaling strategies, including application workloads, databases, and GenAI workloads
• KEDA, Karpenter, service mesh, or multi-cluster architectures
• HLS/DASH packaging or video encoding pipelines