GPU Kernel Engineer

CommonAI CIC

. Write and optimise CUDA and Triton kernels on NVIDIA GPUs for sparse and structured model components, including block-sparse matrix operations and attention variants .

Posted 11/7/2026full-timeCambridge • United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in writing and optimizing CUDA and Triton kernels for NVIDIA GPUs, with a strong understanding of GPU architecture and memory hierarchies. Proficient in profiling and optimizing performance using tools like NVIDIA Nsight, while contributing to open-source projects and adapting kernels for new model designs.

Highest-signal resume keywords

CUDA Kernel OptimizationTriton Kernel DevelopmentNVIDIA Nsight ToolsGPU Architecture UnderstandingAttention Kernel Experience

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Hard Skills

CUDATritonPythonC/C++GPU Kernel OptimizationPerformance ProfilingAttention KernelsSparse KernelsMulti-GPU CommunicationCompiler Stacks

Soft Skills

Rigorous Testing ApproachOpenness to Learning

Tools & Technologies

NVIDIA Nsight ComputeNVIDIA Nsight SystemsCUTLASSCuTeNCCLNVLinkMLIRXLATVM

Industry Keywords

AI AcceleratorsHPCComputer ArchitecturePerformance ModelingOpen-Source Contributions

Tech Stack

Tools & technologies

PythonPyTorchC++

About the role

Key responsibilities & impact

Write and optimise CUDA and Triton kernels on NVIDIA GPUs for sparse and structured model components, including block-sparse matrix operations and attention variants
Adapt and extend open-source kernels, such as FlashAttention-style and block-sparse attention, to new model designs
Profile training and inference to find where time and memory traffic go, using tools such as NVIDIA Nsight Systems and Nsight Compute
Optimise for the GH200's memory hierarchy on Isambard-AI and for the organisation's own GPU nodes
Build tools that measure data movement per generated token, and check analytic cost models against hardware measurements
Test every kernel against reference implementations before it runs at scale
Work with the training infrastructure engineer to raise training throughput, and with researchers to make new designs fast enough to test
Evaluate how models map onto other AI accelerators, such as TPUs and dataflow, wafer-scale and compute-in-memory architectures, and port key components to them where useful
Contribute improvements back to open-source projects where possible

Requirements

What you’ll need

Proven experience writing and optimising GPU kernels in CUDA or Triton, ideally on recent NVIDIA architectures
A strong understanding of GPU architecture and memory hierarchies (e.g. shared memory, HBM bandwidth, memory coalescing, occupancy)
Hands-on experience profiling and optimising performance with tools such as NVIDIA Nsight Compute or Nsight Systems
Strong Python and C/C++
A rigorous approach to testing numerical code (e.g. comparison against reference implementations, mixed-precision error)
Openness to learning new hardware architectures and programming models, often from limited documentation
A strong enthusiasm for using LLMs, coding assistants, and agents as core tools in your own software development process
Experience with attention kernels (e.g. FlashAttention) or sparse kernels (e.g. block sparsity, mixture-of-experts routing)
Experience with NVIDIA Hopper features (e.g. TMA, warpgroup matrix instructions) or libraries such as CUTLASS and CuTe
Experience with multi-GPU communication (e.g. NCCL, NVLink)
An understanding of LLM inference performance (e.g. KV caching, memory-bound decoding, quantisation)
Experience with other AI accelerators (e.g. AMD GPUs, TPUs with JAX and Pallas, or dataflow, wafer-scale and compute-in-memory architectures)
Experience with compiler stacks for AI hardware (e.g. MLIR, XLA, TVM)
A background in computer architecture, HPC, graphics or game engine programming
Experience with hardware/software co-design, or with hardware performance modelling and simulation
Contributions to performance-critical open-source projects (e.g. PyTorch, Triton, JAX, vLLM)

Benefits

Comp & perks

A collaborative and supportive work environment
The opportunity to have a high impact in a growing organisation
Competitive salary package and pension
Professional development opportunities
Networking opportunities with influential people from across the tech sector, financial services, and academia
A vibrant office environment located a few minutes' walk away from Cambridge train station