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ML Infrastructure Engineer

4 months ago
Europe, United States
Senior
Infrastructure Engineer
DevOps and Infrastructure
AWS Docker GCP Kubernetes Machine Learning Python PyTorch
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Nebius

Nebius

Nebius enables B2B companies to build local hyperscaling cloud platforms with cost-effective GPUs, InfiniBand network, and 50% less compute cost. They offer managed Kubernetes and a launch-ready business model for innovative cloud solutions.

Internet Software & Services
51-250
View All Jobs 86

Description

  • Work with hardware and development teams to profile and analyze GPU performance at the system and kernel level.
  • Evaluate and compare GPU performance across different platforms, architectures, and software stacks such as CUDA and ROCm.
  • Debug and optimize ML workloads to run efficiently on GPU hardware by identifying and resolving performance bottlenecks.
  • Perform acceptance testing for new GPU clusters to verify performance, stability, and compatibility for AI workloads.
  • Run experiments across diverse GPU system configurations to assess the impact of interconnect strategies and system-level optimizations.
  • Develop tools and dashboards to visualize performance metrics, bottlenecks, and trends.
  • Contribute to internal tooling, frameworks, and best practices for GPU benchmarking and optimization.

Requirements

  • A strong understanding of the theoretical foundations of machine learning.
  • Deep understanding of performance aspects of large neural network training and inference, including data, tensor, context, and expert parallelism, offloading, custom kernels, hardware features, attention optimizations, and dynamic batching.
  • Deep experience with modern deep learning frameworks such as PyTorch, JAX, Megatron-LM, and TensorRT-LLM.
  • Good understanding of the GPU stack, including CUDA, NCCL, drivers, and relevant libraries.
  • Familiarity with containerized environments such as Docker and Kubernetes.
  • Strong communication skills and the ability to work independently.
  • Familiarity with modern LLM inference frameworks such as vLLM, SGLang, and TensorRT, preferred.
  • Experience with Python and performance profiling tools such as Nsight, nvprof, and perf, preferred.
  • Familiarity with cloud ML platforms such as AWS, GCP, and Azure ML, preferred.
  • Contributions to open-source ML benchmarking tools, preferred.
  • Authorization to work in the country of application, with proof of employment eligibility required at hire.

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.

Interested in this position?

Apply directly on the company website

Apply Now

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