Research Engineer, GPU Performance

Job summary

Responsibilities

As a Research Engineer, GPU performance, you will sit at the heart of that spectrum: your low-level work directly determines how fast our frontier models can be trained and served. Working hand-in-hand with Research Scientists, you'll make every GPU count.

• Develop low-level code that takes full advantage of high-end GPUs and maxes out their capacity — from custom kernels to memory-layout and communication optimisations.
• Rethink parts of the generative model architecture to make them more suitable for efficient inference, in collaboration with Research Scientists.
• Integrate low-level efficient code into a high-level MLOps framework, so that optimisations benefit every training run and deployment rather than a single experiment.
• Profile, benchmark and squeeze performance out of large-scale training and inference on thousands of GPUs.
• Model LLM execution on various GPUs with high fidelity to assess hardware usage and build serving performance estimates.

What they're looking for

• High technical competence for writing custom low-level kernels and pushing GPUs to their limits.
• High expertise in the distributed computation infrastructure of current-generation GPU clusters.
• Overall understanding of the field of generative AI; knowledge of, or interest in, fine-tuning and using language models for applications.
• Strong software-design instincts: testing, code review, CI/CD.
• Self-starter, low-ego, collaborative.