ML/AI Engineer

Nebius AI is an AI cloud platform with one of the largest GPU capacities in Europe. Launched in November 2023, the Nebius AI platform provides high-end, training-optimized infrastructure for AI practitioners. As an NVIDIA preferred cloud service provider, Nebius AI offers a variety of NVIDIA GPUs for training and inference, as well as a set of tools for efficient multi-node training. 

Nebius AI owns a data center in Finland, built from the ground up by the company’s R&D team and showcasing our commitment to sustainability. The data center is home to ISEG, the most powerful commercially available supercomputer in Europe and the 16th most powerful globally (Top 500 list, November 2023).   

Nebius’s headquarters are in Amsterdam, Netherlands, with teams working out of R&D hubs across Europe and the Middle East.  

Nebius AI is built with the talent of more than 500 highly skilled engineers with a proven track record in developing sophisticated cloud and ML solutions and designing cutting-edge hardware. This allows all the layers of the Nebius AI cloud – from hardware to UI – to be built in-house, distictly differentiating Nebius AI from the majority of specialized clouds: Nebius customers get a true hyperscaler-cloud experience tailored for AI practitioners.

We are seeking a highly skilled ML/AI Engineer to join our team to lead and support 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.

Your responsibilities will include: 

• 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

We expect you to have:

• 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

 
We’re growing and expanding our products every day. If you’re up to the challenge and are excited about AI and ML as much as we are, join us!