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Isomorphic Labs

AI-powered drug discovery and development

Staff Software Engineer - Compute Efficiency

Site Reliability EngineeringConfirmed live in the last 24 hours
Full-timeLondon, UKNo salary listed

About the Job

Requirements

  • Possess real-world experience operating, monitoring, and debugging infrastructure for large-scale artificial intelligence and machine learning workloads.
  • Have experience in cloud compute infrastructure design, preferably Google Cloud Platform.
  • Possess strong programming skills.
  • Have significant experience working with and deploying Kubernetes at scale.
  • Be familiar with Nvidia GPU generations.
  • Have a proven track record of building production observability and telemetry stacks.

Responsibilities

  • Design, deploy, and scale robust observability systems and telemetry pipelines to monitor fleetwide compute efficiency, hardware health, and workload goodput across distributed clusters.
  • Drive hardware efficiency and node reliability across the accelerator fleet, and integrate new hardware to leverage advancements.
  • Identify compute waste and efficiency bottlenecks across the fleet, partnering with machine learning and platform teams to optimize accelerator utilization and improve workload goodput.
  • Collaborate with teams in the AI organization and work closely with the machine learning infrastructure team to identify, instrument, and improve canonical efficiency metrics across training and inference runs.
  • Contribute to efforts to consistently improve the reliability of machine learning runs.
  • Operate, maintain, and harden research, development, and production cloud infrastructure and cluster deployments.
  • Partner and collaborate with teams across science, research, product, business development, and operations.
  • Contribute to core technical decisions, including choices of tooling, infrastructure, and architectural design.

Desired Qualifications

  • Have a background in machine learning software engineering or infrastructure site reliability engineering.
  • Have a conceptual understanding of machine learning workload efficiency paradigms.
  • Have experience leading and delivering projects to multidisciplinary stakeholders.
  • Be familiar with Google Tensor Processing Unit generations.
  • Be familiar with workload scheduling, machine learning efficiency research, machine learning-driven research and development cycles, and hardware benchmarking.

About the Company

Isomorphic Labs

AI-powered drug discovery and development

Isomorphic Labs uses artificial intelligence to speed up drug discovery and development. It applies machine learning and computational methods—building predictive and generative models—to accelerate how drugs are designed and how medical research is conducted. The company differentiates itself by combining the AI breakthroughs from Google DeepMind with Alphabet backing, enabling fast experimentation and scaling across biology while maintaining startup-style agility. Its goal is to bring safer, more effective therapies to market more quickly by advancing AI-driven medicine using deep learning, reinforcement learning, and other advanced techniques.

Company size
201-500
Company stage
Series C
Total funding
$7.7B
Headquarters
London, United Kingdom
Founded
2021

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