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Isomorphic Labs is applying frontier AI to drug discovery and development, building on the Nobel-winning AlphaFold system. The company develops powerful predictive and generative AI models to accelerate scientific discovery and design novel molecules.
You will join the Compute Infrastructure team to ensure the company's planet-scale accelerator fleet operates at peak health and efficiency. Working at grand scale, you will design observability systems and automated recovery mechanisms that maximize scientific throughput on every paid GPU-hour.
Key responsibilities:
- Design, deploy, and scale robust observability systems and telemetry pipelines to monitor fleet-wide compute efficiency, hardware health, and workload goodput across distributed clusters
- Drive hardware efficiency and node reliability across the accelerator fleet, integrating new hardware to leverage advancements
- Identify compute waste and efficiency bottlenecks across the fleet, partnering with ML and platform teams to optimize accelerator utilization and improve workload goodput
- Collaborate with teams in the AI organization and work closely with the ML Infrastructure team to identify, instrument, and improve canonical efficiency metrics across training and inference runs
- Contribute to improving the reliability of ML runs
- Operate, maintain, and harden research, development, and production cloud infrastructure and cluster deployments
- Partner with diverse teams including science, research, product, business development, and operations
- Contribute to core technical decisions regarding tooling, infrastructure, and architectural design
The role requires hybrid working with 3 days per week in the London office (Tuesday, Wednesday, and one additional day depending on team assignment).
Requirements:
Essential:
- Real-world experience operating, monitoring, and debugging infrastructure for large-scale AI/ML workloads
- Experience in cloud compute infrastructure design, preferably GCP
- Strong programming skills
- Significant experience working and deploying in Kubernetes at scale
- Familiarity with Nvidia GPU generations
- Proven track record of building production observability and telemetry stacks
Nice to have:
- Background in ML SWE or infrastructure SRE work
- Conceptual understanding of ML workload efficiency paradigms
- Experience leading and delivering projects to multidisciplinary stakeholders
- Familiarity with Google TPU generations
- Familiarity with workload scheduling, machine learning efficiency research, ML-driven R&D cycles, and hardware benchmarking
