ML Infrastructure Engineer

Role description

Role Overview

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.

What You Will Do

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.

Why It Might Be a Fit

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.)

Requirements

• A profound understanding of theoretical foundations of machine learning
• Deep understanding of performance aspects of large neural networks training and inference
• 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

Benefits

• 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