ML Infrastructure Engineer

Responsibilities

• 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

Requirements

• 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

Qualifications

• 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

Skills

• Python
• Performance profiling tools (Nsight, nvprof, perf)
• Cloud ML platforms (AWS, GCP, Azure ML)
• Open-source ML benchmarking tools

Benefits

• 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