ML Infrastructure Engineer

Vaga não está no LinkedIn

🕒 Maio 12

🏄 California – Remoto

🏄 California – Remote

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

👷 Engenheiro de Infraestrutura

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Nebius Group

SiteLinkedInTodas as Vagas

1001 - 5000 funcionários

🤖 Inteligência Artificial

🏢 Corporativo

☁️ SaaS

Artificial Intelligence • Enterprise • SaaS

O Nebius Group está construindo uma das principais empresas de infraestrutura de IA do mundo, focando em fornecer os recursos de computação, armazenamento e ferramentas necessários para desenvolvedores no espaço de IA. Com sede na Europa e listado na Nasdaq, o Nebius tem uma presença global com centros de P&D em toda a Europa, América do Norte e Israel. A principal oferta da empresa é uma plataforma de nuvem centrada em IA projetada para cargas de trabalho intensivas de IA, complementada por vários outros negócios envolvidos em desenvolvimento de IA generativa, edtech e tecnologia autônoma.

Descrição

• 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

🎯 Requisitos

• 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

🏖️ Benefícios

• 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