ML Infrastructure Engineer

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Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy

We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.

Built by engineers, for engineers.

From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel.

Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.

The role We are seeking a highly skilled ML/AI Engineer to join our team to lead and support benchmarking of GPU platforms benchmarking of GPU platforms for machine learning and AI workloads.

You will play a critical role in evaluating the performance of GPU-based hardware for various deep learning and AI frameworks, enabling data-driven decisions for platform optimisation and next-generation hardware development.

Responsabilidades

will include: Work closely with hardware, development teams to profile and analyse GPU performance at the system and kernel level

Evaluate and compare GPU performance across different platforms, architectures, and software stacks (e.

,CUDA, ROCm).

Debug and optimise ML workloads to run efficiently on GPU hardware, identifying and resolving performance bottlenecks.

• Perform acceptance testing acceptance testing for new GPU clusters, ensuring hardware and software meet performance, stability, and compatibility.

Requisitos

for AI workloads

• Perform experiments across diverse GPU system configurations to assess the impact of varying interconnect strategies and system-level optimisations on performance and scalability.

• Develop tools and dashboards to visualise performance metrics visualise perfor.