Requirements:

• An understanding of modern ML techniques and toolsets
• The experience and systems knowledge required to debug 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 Compute 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, and how to use these networking technologies to link up GPU clusters
• An understanding of the collective algorithms supporting distributed GPU training in NCCL or MPI
• An inventive approach and the willingness to ask hard questions about whether we're taking the right approaches and using the right tools
• Fluency in English

Nice to Haves:

• None specified

What You'll Be Doing:

• Optimizing the performance of machine learning models - both training and inference
• Improving CUDA performance with a whole-systems approach
• Ensuring platform efficiency at the lowest level

Perks and Benefits:

• Career opportunities in a rapidly evolving trading environment
• A chance to work with a team of curious minds passionate about solving interesting problems