ML Infrastructure Engineer
# ML Infrastructure Engineer **Nebius** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Mid-level` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[ML-Infrastructure-Engineering]` `[GPU-Engineering]` `[Machine-Learning-Engineering]` `[AI-Infrastructure]` `[Performance-Engineering]` `[ML-Infrastructure-Engineer]` `[ML-AI-Infrastructure-Engineer]` `[AI-ML-Infrastructure-Engineer]` `[Machine-Learning-Infrastructure-Engineer]` `[AI-Infrastructure-Engineer]` `[Machine-Learning-Data-Infrastructure-Engineer]` `[Deep-Learning-Infrastructure-Engineer]` `[AI-ML-Infrastructure-Engineering]` --- ### About the Role 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 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. 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 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 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 work-life balance - 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: 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. Originally posted on Himalayas
- ML-Infrastructure-Engineering
- GPU-Engineering
- Machine-Learning-Engineering
- AI-Infrastructure
- Performance-Engineering
- ML-Infrastructure-Engineer
- ML-AI-Infrastructure-Engineer
- AI-ML-Infrastructure-Engineer
- Machine-Learning-Infrastructure-Engineer
- AI-Infrastructure-Engineer
- Machine-Learning-Data-Infrastructure-Engineer
- Deep-Learning-Infrastructure-Engineer
- AI-ML-Infrastructure-Engineering
Наблюдалась 2026-10-03, впервые 2026-10-02, источник — Himalayas (JSON API).