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Verda

Full-stack AI cloud with GPU compute

Senior Cluster Orchestration Engineer

Platform EngineeringUpdated on Nov 10
Full-timeNo salary listed

About the Job

Requirements

  • At least 5 years of experience in platform engineering, infrastructure engineering, site reliability engineering, or a related field, or equivalent expertise, with a track record of taking projects from design through deployment and maintenance.
  • Strong software development skills in Go or Python, including production Kubernetes controllers or operators, with attention to API design, concurrency, idempotency, retries, and error recovery.
  • Deep knowledge of Kubernetes, including its scheduler, scheduling framework and extension points, control plane, networking, storage, and node lifecycle.
  • Hands-on experience with batch or AI workload schedulers such as Volcano, KAI Scheduler, Kueue, or Slurm Bridge, and a clear understanding of gang scheduling, queueing, quotas, preemption, and fair-share.
  • Experience running GPU workloads, including device plugins, GPU-aware scheduling, and practical limits of multi-node training and latency-sensitive inference.
  • Familiarity with SLURM and an understanding of differences between HPC-style and cloud-native scheduling models.

Responsibilities

  • Design and build the scheduling layer that lets training, inference, and batch workloads share GPU capacity across Kubernetes and SLURM.
  • Build support for perishable compute, including idle reservations, spot-like capacity, and gaps between jobs; fill those windows with short-lived and preemptible work and reclaim capacity cleanly when higher-priority work needs it.
  • Implement gang scheduling, topology-aware placement, and preemption for multi-node training with Volcano, KAI Scheduler, Kueue, or Slurm Bridge; contribute upstream or build custom components where those tools fall short.
  • Develop Kubernetes operators and controllers for quotas, priorities, fair-share, and queueing, as well as cluster lifecycle tasks including creation, scaling, upgrades, health checks, and decommissioning.
  • Solve interactions among elastic and checkpoint-aware training, latency-sensitive inference autoscaling, and batch backfill competing for the same GPUs.
  • Build scheduling and utilization observability, including queue depth, wait times, fragmentation, preemption rates, GPU utilization, and service-level objectives for key platform services.
  • Deliver the platform through GitOps, using declarative configuration, drift detection, and safe rollout and recovery.

Desired Qualifications

  • Go is preferred over Python.

About the Company

Verda

Full-stack AI cloud with GPU compute

Verda provides a full-stack AI cloud infrastructure in Europe and beyond, combining the physical data centers and hardware with a cloud platform and AI research capabilities. It offers on-demand and reserved GPU instances, serverless containers, and managed inference on vertically integrated European infrastructure powered by renewable energy. The company serves AI and software teams who need self-service compute without long sales cycles. Verda is an NVIDIA Preferred Partner and operates across Europe, the UK, the US, and Asia. Its goal is to multiply compute capacity, deepen inference and platform services, and expand internationally while maintaining a strong growth trajectory.

Company size
201-500
Company stage
Series B
Total funding
$513.7M
Headquarters
Helsinki, Finland
Founded
2020

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