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# Software Engineer (Scheduler)

- **Company:** Isomorphic Labs
- **Location:** London, United Kingdom
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Computer Programming, DevOps, Python (Programming Language), Machine Learning, Graphics Processing Unit (GPU), High Performance Computing, System Availability, Kubernetes, Build Tools, Slurm, Machine Learning Operations
- **Published:** November 7, 2026
- **Apply:** https://startup.jobs/software-engineer-scheduler-london-isomorphic-labs-10713814

## About the Role

* Scheduling Frameworks: Practical experience building or operating cluster schedulers, batch systems, or queuing frameworks (e.g., Kueue, Volcano, Slurm).
 * Deep Kubernetes Expertise: Deep, hands-on Kubernetes experience, moving beyond standard usage to write and maintain custom K8s controllers, operators, or interacting directly with cluster orchestration APIs. Strong familiarity with K8s scheduler.
 * Cloud Infrastructure Experience: Strong practical experience with cloud providers, focusing on compute provisioning, networking, and Infrastructure as Code to build scalable foundational environments.
 * Adaptability: A solid foundation in DevOps/SRE best practices, with the ability to adapt these principles to the dynamic nature of AI accelerator infrastructure.
 * Programming Proficiency: Strong programming skills, with high proficiency in Python or Go., * Experience operating Kueue.
 * Familiarity with ML infrastructure nuances: Nvidia GPUs, Google TPUs, gang scheduling, or collective networking.
 * Previous experience in High-Performance Computing or maximizing utilization in environments with fixed/committed hardware constraints.
 * Familiarity with GCP cloud compute infrastructure and Infrastructure as Code.
 * Background in ML SWE or infrastructure SRE work supporting machine learning research activities.

## Description

You will join our newly formed Scheduling team to own and extend the systems that orchestrate compute allocation to complex machine learning workloads running on Kubernetes. You will be responsible for optimizing our compute footprint, ensuring that dynamic batch inference and large-scale training runs are scheduled reliably and efficiently.

We are looking for an engineer who operates comfortably at the intersection of distributed systems and modern ML infrastructure. You will design systems that support large-scale, topology-sensitive workloads, optimize batch queuing frameworks and write custom Kubernetes controllers. Your work will directly determine our ability to maximize hardware utilization and accelerate the pace of AI-driven drug discovery., * Own the Scheduling Layer: Design, operate, and extend ML workload scheduling systems to handle dynamic batch inference and long-running training workloads.

 * Optimize Compute Utilization: Drive the implementation of scheduling policies like gang scheduling, topology-aware placement, and preemption to ensure efficient use across our fleets of committed GPUs and TPUs.
 * Partner with Research: Collaborate directly with ML researchers and the broader Infrastructure team to understand workload behavior, troubleshoot bottlenecks, and turn research requirements into platform capabilities.
 * Ensure Reliability: Build systems with clear failure semantics. Participate in the operational lifecycle of the scheduling ecosystem, designing robust processes, runbooks, and SLOs to maintain high availability.
 * Build Custom Infrastructure: Build and maintain custom Kubernetes controllers, operators, and CRDs that adapt the cluster orchestration layer to the specific requirements of our ML pipelines.