About
Nebius builds a full-stack AI cloud platform that supports developers and enterprises from data and model training through production deployment. Its infrastructure spans compute, storage, networking, and applied AI.
Responsibilities
•Profile and analyze GPU performance at the system and kernel level with hardware and development teams
•Evaluate and compare GPU performance across platforms, architectures, and software stacks such as CUDA and ROCm
•Debug and optimize ML workloads for GPU hardware, identifying and resolving performance bottlenecks
•Perform acceptance testing for new GPU clusters to ensure hardware and software meet performance, stability, and compatibility requirements for AI workloads
•Run experiments across GPU system configurations to assess how interconnect strategies and system-level optimizations affect performance and scalability
•Develop tools and dashboards to visualize performance metrics, bottlenecks, and trends
•Contribute to internal tooling, frameworks, and best practices
Requirements
•Strong understanding of the theoretical foundations of machine learning
•Deep understanding of performance aspects of large neural network training and inference, including parallelism, offloading, custom kernels, hardware features, attention optimizations, and dynamic batching
•Deep experience with modern deep learning frameworks, including 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 ability to work independently
Nice to have
•Familiarity with modern LLM inference frameworks such as vLLM, SGLang, and TensorRT
•Experience with Python and performance profiling tools such as Nsight, nvprof, and perf
•Familiarity with cloud ML platforms such as AWS, GCP, and Azure ML
•Contributions to open-source ML benchmarking tools
Conditions
•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
•Applicants must be authorized to work in the country in which they apply and provide proof of employment eligibility as a condition of hire
