Senior ML Infrastructure Engineer

Join us at EIT:

At the Ellison Institute of Technology (EIT), we’re on a mission to translate scientific discovery into real world impact. We bring together visionary scientists, technologists, policy makers, and entrepreneurs to tackle humanity’s greatest challenges in four transformative areas:

• Health, Medical Science & Generative Biology
• Food Security & Sustainable Agriculture
• Climate Change & Managing CO₂
• Artificial Intelligence & Robotics

This is ambitious work - work that demands curiosity, courage, and a relentless drive to make a difference. At EIT, you’ll join a community built on excellence, innovation, tenacity, trust, and collaboration, where bold ideas become real-world breakthroughs. Together, we push boundaries, embrace complexity, and create solutions to scale ideas for lab to society. Explore more at www.eit.org

Your Role:

Join our SciComp team to build the cloud and compute foundation that enables scientific breakthroughs. Deliver reliable, secure platforms and self-service guardrails that accelerate experimentation and turn ideas into results - faster, at scale, and with confidence.

Your Responsibilities:

• Build, operate, and continuously optimise our high-performance GPU training and inference clusters, focusing on robust, high-availability scheduling, isolation, and automated lifecycle management.
• Drive systems design and implementation for high-throughput data paths, optimising I/O, caching, and data locality across compute and storage (including our current Lustre implementation).
• Proactively benchmark, profile, and resolve performance bottlenecks across the compute, network, and orchestration layers to maximise efficiency for distributed training and inference.
• Establish comprehensive observability, resilience, and automated security controls to ensure compliance and robust operation of sensitive research environments.
• Partner with Research, Data, and Applied teams to forecast capacity and cost for GPU and storage needs, setting quotas and streamlining ML experimentation pipelines.