ML Infrastructure Engineer - Scheduling & Kubernetes

Isomorphic Labs; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

Isomorphic Labs in the United Kingdom is seeking a Scheduling engineer to own ML workload orchestration on Kubernetes, optimizing compute and enabling reliable, large‑scale training and batch inference. You will design systems that maximize hardware utilization and collaborate with ML researchers and the Infrastructure team to turn research into platform capabilities.

You will build custom Kubernetes controllers, operators, and CRDs, implement topology-aware placement, and contribute to robust

Qualifications

• Experience with ML infrastructure and large-scale orchestration.
• Familiarity with HPC concepts and GPU/TPU scheduling is a plus.
• Knowledge of cloud IaC, GCP, and Kubernetes tooling.
• Background in ML software engineering or infra SRE is desirable.

Responsibilities

• Own the Scheduling Layer: Design, operate, and extend ML workload scheduling systems.
• Optimize Compute Utilization: Implement policies for efficient GPU/TPU usage.
• Partner with Research: Collaborate with ML researchers to translate requirements.
• Ensure Reliability: Define SLOs and robust runbooks for scheduling systems.
• Build Custom Infrastructure: Develop Kubernetes controllers, operators, and CRDs.
• Scheduling Frameworks: Work with cluster schedulers and queuing systems.
• Deep Kubernetes Expertise: Write and maintain custom K8s controllers and APIs.
• Cloud Infrastructure Experience: Provision, network, and IaC for scalable environments.
• Adaptability: Apply DevOps/SRE best practices to AI accelerator infra.
• Programming Proficiency: Strong Python or Go skills.

Skills

Python

Go

Kubernetes

DevOps

SRE

ML infrastructure

Tools

Kubernetes controllers

CRDs

Terraform

GCP

IaC

Job description

Isomorphic Labs in the United Kingdom is seeking a Scheduling engineer to own ML workload orchestration on Kubernetes, optimizing compute and enabling reliable, large‑scale training and batch inference. You will design systems that maximize hardware utilization and collaborate with ML researchers and the Infrastructure team to turn research into platform capabilities.

You will build custom Kubernetes controllers, operators, and CRDs, implement topology-aware placement, and contribute to robust