Key skills
Machine learning theoretical foundationsNeural network training and inference optimizationPyTorchJAXMegatron-LMTensort-LLMCUDANCCLGPU drivers and librariesDockerKubernetesPythonAIMachine LearningMLDeep LearningLLMAWSAzureGCPCommunication
About this role
Nebius is leading a new era in cloud infrastructure for the global AI economy. The role involves leading and supporting benchmarking of GPU platforms for machine learning and AI workloads, evaluating GPU performance, and contributing to platform optimisation and hardware development.
Responsibilities:
- 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 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 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
- Familiarity with modern LLM inference frameworks (vLLM, SGLang, TensorRT)
- Experience in Python and performance profiling tools (e.g., Nsight, nvprof, perf)
- Familiarity with cloud ML platforms like AWS, GCP, Azure ML
- Contributions to open-source ML benchmarking tools