Senior Applied Research Engineer
Fundamental, Barcelona
What you will do
- Profile end-to-end distributed training runs to identify bottlenecks across compute, GPU memory, and inter-GPU communication
- Contribute to architectural decisions that improve the efficiency and reliability of large-scale training jobs, including developing Triton/CUDA kernels when needed
- Design and implement model scaling, parallelization, and memory optimization techniques for training workloads with very large context sizes
- Collaborate closely with ML Researchers to diagnose architectural inefficiencies, ensure new research ideas scale efficiently in practice, and spread internal knowledge about model efficiency and optimization
