Senior Cluster Orchestration Engineer

Verda; Helsinki; Remote; EUR 90,000 - 170,000; Full time

Benefits offered by this job

Cash and equity

Global team

Impactful work

Job summary

Verda is hiring a Senior Cluster Orchestration Engineer to build the shared GPU capacity layer that unifies training, inference and batch workloads across Kubernetes and SLURM.

You will write schedulers, controllers and platform services, collaborating with GPU fleet engineers and product teams. The role offers direct architecture influence on state-of-the-art hardware at scale and a path from idea to production.

Qualifications

• 5+ years in platform engineering or related field, with design-to-deploy experience.
• Strong Go or Python development skills and Kubernetes controller experience.
• Deep Kubernetes knowledge: scheduling framework, control plane, networking and storage.
• Hands-on with batch or AI schedulers like Volcano, KAI Scheduler, Kueue or Slurm Bridge.

Responsibilities

• Design and build the scheduling layer to share GPU capacity across Kubernetes and SLURM.
• Add support for perishable compute and short-lived, preemptible workloads.
• Implement gang scheduling and topology-aware placement for multi-node training.
• Develop Kubernetes operators for quotas, priorities, and queueing; manage cluster lifecycle.

Skills

Go

Python

Kubernetes

API design

Concurrency

Distributed systems

Tools

Volcano

KAI Scheduler

Kueue

Slurm Bridge

Job description

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.

Join Verda while it’s still being built - not once it’s finished.

Why Verda

• Cash and equity compensation along with local benefits.

• 40+ nationalities, with 6 different ones on the management team.

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

About the role

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.

Your 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: 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.

• Deliver the platform through GitOps, with declarative configuration, drift detection and safe rollout and recovery.

Your key competencies

• 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.

• Familiarity with SLURM, and an understanding of how HPC‑style and cloud‑native scheduling models differ.

Practicalities

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

• Employment type: Full time and permanent