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# ML Infrastructure Engineer

- **Company:** Nebius
- **Location:** Amsterdam, Netherlands (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Artificial Neural Networks, Computer Clusters, Profiling, Nvidia CUDA, Software Debugging, Hardware Design, Python (Programming Language), Machine Learning, Open Source Technology, Azure Machine Learning, Google Cloud, Pytorch, Large Language Models, Deep Learning, Parallel Computation, Perf (Linux), Containerization, Kubernetes, Data Analytics, Machine Learning Operations, TensorRT, Docker
- **Published:** July 15, 2026
- **Apply:** https://www.adzuna.nl/details/5728635035

## About the Role

* 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

Ways to stand out from the crowd:

 * 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, Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.

## Description

We are seeking a highly skilled ML/AI Engineer to join our team to lead and support benchmarking of GPU platforms benchmarking of GPU platforms for machine learning and AI workloads. You will play a critical role in evaluating the performance of GPU-based hardware for various deep learning and AI frameworks, enabling data-driven decisions for platform optimisation and next-generation hardware development., * 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


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