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ML Infrastructure Engineer

Nebius· Technology Product
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Hybrid (Amsterdam, Netherlands; Remote - Europe; Remote - United States)Posted 5mo ago

About the role

We are seeking a highly skilled ML/AI Engineer to lead and support benchmarking of GPU platforms for machine learning and AI workloads, evaluating GPU-based hardware performance to inform platform optimization and next-generation hardware development.

Responsibilities

  • Work closely with hardware and 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.g., CUDA, ROCm).
  • Debug and optimise ML workloads to run efficiently on GPU hardware, identifying and resolving performance bottlenecks.
  • Perform acceptance testing for new GPU clusters, ensuring hardware and software meet performance, stability, and compatibility requirements 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, bottlenecks, and trends.
  • Contribute to internal tooling, frameworks, and best practices

Qualifications

  • A profound understanding of theoretical foundations of machine learning
  • Deep understanding of performance aspects of large neural networks training and inference (data/tensor/context/expert parallelism, offloading, custom kernels, hardware features, attention optimisations, dynamic batching etc.)
  • Deep experience with modern deep learning frameworks (PyTorch, JAX, Megatron-LM, Tensor-LLM)
  • Good understanding of the GPU stack: CUDA, NCCL, drivers, and relevant libraries
  • Familiarity with containerized environments (e.g., Docker, Kubernetes)
  • Strong communication and ability to work independently

Skills

Required:Theoretical foundations of machine learningPerformance optimization for large neural networks (training and inference)Modern deep learning frameworksGPU stack fundamentalsContainerizationCommunication and independent work
Nice to have:Performance profiling tools (Nsight, nvprof, perf)

Benefits

  • Competitive compensation
  • Career growth and learning opportunities
  • Flexibility and ownership
  • Collaborative and innovative culture
  • Opportunity to work on impactful AI projects
  • International environment and talented teams
Last updated Jul 8, 2026. Interested? Apply on the company site.
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The ultimate cloud for AI explorers

Discover the most efficient way to build, tune and run your AI models and applications on top-notch NVIDIA® GPUs.

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