ML Infrastructure Engineer

Job not on LinkedIn

🕒 May 12

🌐 United States, Netherlands – Remote

🇺🇸 United States – Remote 🇳🇱 Netherlands – Remote

🏄 California – Remote

🏄 California – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

👷 Infrastructure Engineer

👻 Ghost score 45%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Nebius Group

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

🤖 Artificial Intelligence

🏢 Enterprise

☁️ SaaS

Artificial Intelligence • Enterprise • SaaS

Nebius Group is building one of the world’s leading AI infrastructure companies, focusing on providing the necessary compute, storage, and tools for developers in the AI space. Based in Europe and listed on Nasdaq, Nebius has a global presence with R&D centers across Europe, North America, and Israel. The company's primary offering is an AI-centric cloud platform designed for intensive AI workloads, complemented by various other businesses involved in generative AI development, edtech, and autonomous technology.

📋 Description

• 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.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

🎯 Requirements

• 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, Tensort-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

🏖️ Benefits

• Competitive compensation • Career growth and learning opportunities • Flexibility and work-life balance • Collaborative and innovative culture • Opportunity to work on impactful AI projects • International environment and talented teams