Hybrid ML Scheduler Engineer (Kubernetes)

Isomorphic Labs; City Of London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Isomorphic Labs in the United Kingdom is seeking an engineer to join the Scheduling team responsible for orchestrating compute for large ML workloads on Kubernetes. You will design and extend scheduling systems to support dynamic batch inference and long-running training runs, with a focus on reliability and efficiency.

You will work at the intersection of distributed systems and ML infra, collaborating with researchers and the broader Infrastructure team to translate workload needs into

Qualifications

• Scheduling frameworks experience building or operating cluster schedulers, batch systems, or queuing frameworks (e.g., Kueue, Volcano, Slurm).
• Deep Kubernetes expertise with custom controllers/operators and API interaction; strong scheduler knowledge.
• Cloud provisioning, networking and IaC to build scalable environments.
• DevOps/SRE fundamentals adapted to AI accelerator infrastructure.
• Strong programming skills in Python or Go.

Responsibilities

• Own the Scheduling Layer: design, operate and extend ML workload scheduling for dynamic batch inference and long-running training.
• Optimize Compute Utilization: implement policies like gang scheduling and topology-aware placement across GPUs/TPUs.
• Partner with Research: collaborate with ML researchers and Infrastructure to translate workload needs into platform capabilities.
• Ensure Reliability: build systems with clear failure semantics and maintain high-availability through SLOs.
• Build Custom Infrastructure: develop Kubernetes controllers, operators and CRDs for ML pipelines.

Skills

Scheduling frameworks

Kubernetes expertise

Cloud infrastructure

DevOps/SRE

Python/Go

Job description

Isomorphic Labs in the United Kingdom is seeking an engineer to join the Scheduling team responsible for orchestrating compute for large ML workloads on Kubernetes. You will design and extend scheduling systems to support dynamic batch inference and long-running training runs, with a focus on reliability and efficiency.

You will work at the intersection of distributed systems and ML infra, collaborating with researchers and the broader Infrastructure team to translate workload needs into