GPU Kernel & Transformer Performance Scientist

Lumaai; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Luma seeks a seasoned performance engineer to accelerate multimodal models across GPU, CPU, and accelerators. You will write high‑performing PyTorch, Triton, and CUDA kernels and push hardware to the limit while preserving model quality.

You’ll own profiling, optimization, and deployment at scale, develop fused kernels, tensor-core aware code, and build monitoring tools for distributed training and inference.

Qualifications

• Expert-level Triton/CUDA programming and GPU optimization.
• Strong PyTorch skills, including kernel development and custom operations.
• Proficiency with profiling tools (NVIDIA Nsight, torch profiler, custom tooling).
• Deep understanding of transformer architectures and attention mechanisms.
• Nice to Have: experience with compilers and exporters (torch.compile, TensorRT, ONNX, XLA).

Responsibilities

• Profile and optimize GPU/CPU/accelerator code for maximum utilization and minimal latency.
• Write high-performance PyTorch, Triton, and CUDA, dropping to custom operations when needed.
• Develop fused kernels and leverage tensor cores and modern hardware features across platforms.
• Optimize model architectures and implementations for distributed multi-node production deployment.
• Build performance monitoring and analysis tools and automation.
• Research and implement cutting‑edge optimization techniques for transformer models.

Skills

Triton/CUDA programming

GPU optimization

PyTorch kernel development

Profiling tools

Tools

NVIDIA Nsight

Torch profiler

Custom tooling

Job description

Luma seeks a seasoned performance engineer to accelerate multimodal models across GPU, CPU, and accelerators. You will write high‑performing PyTorch, Triton, and CUDA kernels and push hardware to the limit while preserving model quality.

You’ll own profiling, optimization, and deployment at scale, develop fused kernels, tensor-core aware code, and build monitoring tools for distributed training and inference.