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Cohere

Senior

Lead architecture for Cohere's Model Serving platform running LLMs at scale

This is a senior/lead infrastructure role on Cohere's Model Serving team, setting architecture and technical direction for serving Cohere's LLMs via low-latency, high-throughput, high-availability API endpoints on Kubernetes and GPUs across multiple clouds. Expect deep distributed systems, Kubernetes/GPU, and multi-cloud cost/ops questions plus technical leadership scope.

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这场面试考什么

  • Distributed systems architecture on Kubernetes with GPU workloads
  • Multi-cloud and hybrid/on-prem serving strategy (GCP/Azure/AWS/OCI)
  • Accelerator (GPU/TPU) performance characteristics for latency/throughput
  • Org-level compute/storage/network cost and optimization ownership
  • Technical leadership: setting standards, senior-level code review, mentoring
  • Customer-facing technical leadership for custom deployments

常见提问方向

Design a low-latency, high-throughput LLM serving system on Kubernetes with GPU workloads

How do you decide between multi-cloud vs on-prem for a large-scale inference deployment

Explain how you'd optimize throughput given accelerator compute characteristics

Walk through how you've owned infra cost management and driven savings at an org level

Tell me about setting engineering standards and reviewing senior engineers' designs

How would you lead a customized deployment for an enterprise customer with unique requirements

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