ML Infrastructure Engineer (opens in a new tab) Amsterdam · Remote - Europe · Remote - United States

Nebius Group; Amsterdam; Hybrid; EUR 75,000 - 110,000; Full time

Benefits offered by this job

Competitive compensation

Career growth and learning

Flexibility and ownership

Collaborative culture

Impactful AI projects

Job summary

Nebius is seeking a highly skilled ML/AI Engineer to lead benchmarking of GPU platforms for machine learning and AI workloads. You will evaluate performance across architectures and software stacks, profile at system and kernel levels, and develop tooling to drive platform optimization.

You will work closely with hardware and development teams to debug bottlenecks, perform experiments, and ensure scalable performance for next-generation AI infrastructure.

Qualifications

• Strong ML theory foundations.
• Deep experience with training and inference of large neural networks.
• Experience with PyTorch/TensorFlow/JAX.
• Knowledge of CUDA and GPU driver stack.
• Familiarity with Docker and Kubernetes.
• Excellent communication and ability to work independently.

Responsibilities

• Profile and analyze GPU performance at the system and kernel level.
• Evaluate and compare GPU performance across platforms, architectures, and software stacks (CUDA, ROCm).
• Debug and optimise ML workloads to run efficiently on GPU hardware, identifying bottlenecks.
• Perform acceptance testing for new GPU clusters, ensuring performance, stability and compatibility for AI workloads.
• Develop tools and dashboards to visualise performance metrics, bottlenecks, and trends.
• Contribute to internal tooling, frameworks, and best practices.

Skills

ML theory

PyTorch

GPU profiling

Python

Communication

Tools

Docker

Kubernetes

CUDA

Nsight

nvprof

Job description

About Nebius:

Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.

Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.

The role

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

Your responsibilities will include:

• Work closely with hardware, development teams to profile and analyse GPU performance at the system and kernellevel.
• 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 performancebottlenecks.
• Perform acceptance testingacceptance 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 interconnectstrategies and system-level optimisations on performance and scalability.
• Develop tools and dashboards to visualise performance metricsvisualise 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

Benefits & Perks:

• 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

What's it like to work at Nebius:

Fast moving- Bold thinking- Constant growth- Meaningful impact- Trust and real ownership- Opportunity to shape the future of AI

Equal Opportunity Statement:

Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.

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.

If you need accommodations during the application process, please let us know.