Software Engineer (Scheduler), London

Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed.

Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases.

We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design.

Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI.

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.

• 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.

We are guided by our shared values. It's not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it. 

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