Machine Learning Performance Engineer

We are looking for an engineer with experience in low-level systems programming and optimisation to join our growing ML team. The engineer will be responsible for optimising the performance of our models, including training and inference, using a whole-systems approach.

Requirements

• Understanding of modern ML techniques and toolsets
• Experience in debugging a training run's performance end to end
• Low-level GPU knowledge of PTX, SASS, warps, cooperative groups, Tensor Cores and the memory hierarchy
• Debugging and optimisation experience using tools like CUDA GDB, NSight Systems, NSight Computesight-systems and nsight-compute
• Library knowledge of Triton, CUTLASS, CUB, Thrust, cuDNN and cuBLAS
• Intuition about the latency and throughput characteristics of CUDA graph launch, tensor core arithmetic, warp-level synchronization and asynchronous memory loads
• Background in Infiniband, RoCE, GPUDirect, PXN, rail optimisation and NVLink
• Understanding of the collective algorithms supporting distributed GPU training in NCCL or MPI
• Fluency in English