Senior-level experience in training machine learning models at scale, in industry or a research group of comparable intensity, instrumental in:
distributed training with FSDP or equivalent, including profiling and diagnosing idle GPUs;
PyTorch and CUDA at depth, ideally including sparse convolution or sparse attention internals and custom CUDA or Triton kernels;
implementing architectures from papers and making them work, then making them fast;
reproducible experiment infrastructure, because most of the compute in a programme like this is spent on runs that fail.
Experience with 3D or other irregular data: meshes, point clouds, voxels, sparse tensors.
Ability to scope and deliver projects, and strong problem-solving skills.
Degree in CS, engineering, mathematics, physics or a related field. A PhD is welcome but not required; engineering depth matters more.
Desirable: open-source contributions, public technical writing, or papers at MLSys, SIGGRAPH or CVPR

Senior Machine Learning Software Engineer, Research

Summary