Member of Technical Staff - Training Infrastructure

- Demonstrated proficiency with distributed training frameworks and techniques (e.g. FSDP, DeepSpeed, Megatron, Pytorch, JAX/XLA) to train large foundation models

- Strong grasp of state-of-the-art techniques for optimizing training workloads: parallelism strategies, memory optimization, mixed precision, communication overlap

- Ability to profile and debug performance in complex codebases, from framework internals down to kernels and collectives

- Deep understanding of deep learning frameworks (e.g. PyTorch, JAX) and their underlying system architectures

- Bonus: contributions to open-source ML infrastructure (e.g. PyTorch, Megatron-LM, DeepSpeed, XLA)