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Senior Site Reliability Engineer — Token Factory (Inference Platform)

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Remote from
Europe
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
27 Nov 2026
Listing views
50
Application actions
2
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AI Summary

The role, at a glance.

Nebius is seeking a Senior Site Reliability Engineer to own reliability, performance, and observability for its large-scale AI inference platform. The role focuses on Kubernetes-based GPU infrastructure, telemetry, autoscaling, infrastructure as code, incident response, and service-level objectives for high-throughput APIs. The engineer will build self-healing systems, improve request routing and resilience, and lead post-incident learning. Strong production experience with distributed systems, Prometheus, Grafana, Terraform, Python or Bash, and GPU-heavy workloads is central to success. This is a senior, high-impact infrastructure role in a fast-moving AI cloud environment.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe role requires deep production SRE expertise across distributed systems, Kubernetes, GPU workloads, observability, and infrastructure automation. It also carries ownership for reliability under extreme load and rapid incident remediation.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$190,000
US market range (per year)$160k–$240k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. Estimated annual USD compensation for a senior site reliability engineer working on AI inference and GPU cloud infrastructure is approximately $160,000–$240,000, with a midpoint estimate of $190,000; this is an AI estimate rather than verified market research.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you define SLOs for a high-throughput model inference API?

I would start from user-visible outcomes such as successful response rate, latency percentiles, and availability. I would define SLIs from request metrics, set realistic error budgets, segment measurements by model or endpoint where needed, and connect alerts to budget burn rates rather than isolated infrastructure symptoms.

Describe how you would investigate a sudden increase in inference latency on a GPU cluster.

I would correlate API latency with queue depth, GPU utilization, memory pressure, pod scheduling, network metrics, and downstream dependency health. I would use traces and logs to isolate the affected path, compare healthy and unhealthy workloads, validate autoscaler behavior, and apply a mitigative action before conducting a post-incident root-cause analysis.

What practices would you use to make Terraform-managed infrastructure resilient?

I would build composable, reviewed modules with explicit inputs and safe defaults, keep state access controlled, and use automated plan and policy checks in CI. I would also design for multi-zone failure handling, enforce tagging and observability standards, and test changes in non-production environments before rollout.

How do you approach alert design to avoid alert fatigue?

I alert on actionable, user-impacting conditions and use symptoms such as error-budget burn or sustained latency degradation as primary signals. Alerts should have clear severity, ownership, runbook links, sensible deduplication, and regular review to remove noisy or non-actionable rules.

How would you improve reliability for GPU-heavy workloads managed through Kubernetes?

I would use resource requests and limits appropriate to GPU scheduling, node and device health monitoring, capacity-aware autoscaling, and workload isolation where necessary. I would also monitor GPU utilization and memory, test failure recovery, implement graceful retries and routing, and ensure model-serving services have robust readiness and rollback mechanisms.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

About Nebius:

Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.

Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.

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.

In this role you will own the reliability, performance, and observability of the entire inference stack. Your day starts with designing and refining telemetry pipelines — metrics, logs, and traces that turn hundreds of terabytes of signal into clear, actionable insight. From there you might tune Kubernetes autoscalers to squeeze more efficiency out of GPUs, craft Terraform modules that bake resilience into every new cluster, or harden our request-routing and retry logic so even transient failures go unnoticed by users. When incidents do arise, you’ll rely on the automation and runbooks you helped create to detect, isolate, and remediate problems in minutes, then drive the post-mortem culture that prevents recurrence. All of this effort points toward a single goal: scaling the platform smoothly while hitting aggressive cost and reliability targets.

Success in the role calls for deep fluency with Kubernetes, Prometheus, Grafana, Terraform, and the craft of infrastructure-as-code. You script comfortably in Python or Bash, understand the nuances of alert design and SLOs for high-throughput APIs, and have spent enough time in production to know how distributed back-ends fail in the real world. Experience shepherding GPU-heavy workloads — whether with vLLM, Triton, Ray, or another accelerator stack — will serve you well, as will a background in MLOps or model-hosting platforms. Above all, you care about building self-healing systems, thrive on debugging performance from kernel to application layer, and enjoy collaborating with software engineers to turn reliability into a feature users never have to think about.

If the idea of safeguarding the infrastructure that powers tomorrow’s multimodal AI energizes you, we’d love to hear your story.

Benefits & Perks:

  • 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

What’s it like to work at Nebius:

Fast moving – Bold thinking – Constant growth – Meaningful impact – Trust and real ownership – Opportunity to shape the future of AI

Equal Opportunity Statement:

Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.

Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.

If you need accommodations during the application process, please let us know.

Apply now >

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