Nebius

Site reliability engineer in inference platform

In short

This role owns reliability, performance, and observability of an AI inference platform running on tens of thousands of GPUs, including telemetry pipelines, autoscaling, and request-routing resilience. The company is Nebius Cloud's Token Factory, building infrastructure for serving foundation models at massive scale. It demands deep fluency with Kubernetes, Prometheus, Grafana, Terraform, and production experience with distributed systems. The position is fully remote, senior level, requiring at least 3 years of experience.

About
Token Factory is a part of Nebius Cloud, one of the world's largest GPU clouds, running tens of thousands of GPUs. We are building an inference platform that makes every kind of foundation model — text, vision, audio, and emerging multimodal architectures — fast, reliable, and effortless to deploy at massive scale. To deliver on that promise, we need an engineer who can make the platform behave flawlessly under extreme load and recover gracefully when the unexpected happens.
Responsibilities
•Own the reliability, performance, and observability of the entire inference stack
•Design and refine telemetry pipelines — metrics, logs, and traces that turn hundreds of terabytes of signal into clear, actionable insight
•Tune Kubernetes autoscalers to squeeze more efficiency out of GPUs
•Craft Terraform modules that bake resilience into every new cluster
•Harden request-routing and retry logic so even transient failures go unnoticed by users
•Rely on the automation and runbooks you helped create to detect, isolate, and remediate problems in minutes
•Drive the post-mortem culture that prevents recurrence
•Scale the platform smoothly while hitting aggressive cost and reliability targets
Requirements
•Deep fluency with Kubernetes, Prometheus, Grafana, Terraform, and the craft of infrastructure-as-code
•Script comfortably in Python or Bash
•Understand the nuances of alert design and SLOs for high-throughput APIs
•Have spent enough time in production to know how distributed back-ends fail in the real world
•Care about building self-healing systems
•Thrive on debugging performance from kernel to application layer
•Enjoy collaborating with software engineers to turn reliability into a feature users never have to think about
Nice to have
•Experience shepherding GPU-heavy workloads — whether with vLLM, Triton, Ray, or another accelerator stack
•A background in MLOps or model-hosting platforms
Conditions
•Competitive compensation
•Career growth and learning opportunities
•Flexibility and ownership
•Collaborative and innovative culture
•Opportunity to work on impactful AI projects
•International environment and talented teams
•Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI

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