Senior Cluster Orchestration Engineer

At Verda, we're building a full-stack AI cloud, covering everything from data centers and hardware to our own cloud platform that

the world's leading AI teams use to do serious AI work.

We strive to make a positive mark on the world through the infrastructure we build and give leading teams a service they can truly

depend on. Headquartered in Helsinki, we operate globally with offices in London and San Francisco.

• A real chance to make an impact and work alongside world class engineers, researchers, and partners across the global AI

ecosystem.

Most GPU clusters are split into silos. Capacity sits idle in one silo while another is starved. As a Senior Cluster Orchestration

Engineer, you build the layer that turns these silos into one shared cluster, where training, inference and batch workloads run on

the same GPUs and capacity moves between them automatically.

This is a software development role in our AI Infrastructure team. You write the schedulers, controllers and platform services

that make shared capacity work on Kubernetes and SLURM. You work closely with the engineers who run our GPU fleets and with the

teams building customer products on top. The team is small, so you have direct influence on the architecture and a short path from

idea to production, on state-of-the-art GPU hardware at real scale.

• 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: capacity that is free only for a short window, such as idle reservations, spot-like

capacity or gaps between jobs. Fill it with short-lived and preemptible work, and reclaim it 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 these tools fall short.

• Develop Kubernetes operators and controllers for quotas, priorities, fair-share and queueing, and for the cluster lifecycle:

creation, scaling, upgrades, health checks and decommissioning.

• Solve the hard interactions between workload types: elastic and checkpoint-aware training, latency-sensitive inference

autoscaling and batch backfill, all competing for the same GPUs.

• Build observability for scheduling and utilisation, including queue depth, wait times, fragmentation, preemption rates, GPU

utilisation and SLOs for key platform services.

• 5+ years in platform engineering, infrastructure engineering, SRE or a related field, or equivalent expertise, and a track

record of taking projects from design through deployment and maintenance.

• Strong software development skills in Go (preferred) or Python, including production Kubernetes controllers or operators. You

pay attention to API design, concurrency, idempotency, retries and error recovery.

• Deep Kubernetes knowledge: the scheduler, the scheduling framework and its extension points, the 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 the practical limits of multi-node

training and latency-sensitive inference.

• Location: London (UK) or Helsinki (Finland), where we have offices. If you're based elsewhere in Europe, you can also work

remotely.

We're building fast and this role needs the right person behind it. There's no artificial deadline, but when we find who we're

looking for, we move. If this sounds like your next move, apply now.