SoftwareEngineer-AI/ML,AWSNeuronDistributedTraining

Annapurna Labs designs silicon and software that accelerates innovation. Our custom chips, accelerators, and software stacks enable us to tackle unprecedented technical challenges and deliver solutions that help customers change the world. AWS Neuron is the complete software stack powering AWS Trainium (Trn2/Trn3), our cloud scale Machine Learning accelerators and we are seeking a Senior Software Engineer to join our ML Distributed Training team. In this role, you will be responsible for the development, enablement, and performance optimization of large scale ML model training across diverse model families. This includes massive scale pre-training and post-training of LLMs with Dense and Mixture-of-Experts architectures, Multimodal models that are transformer and diffusion based, and Reinforcement Learning workloads. You will work at the intersection of ML research and high performance systems, collaborating closely with chip architects, compiler engineers, runtime engineers and AWS solution architects to deliver cost-effective, performant machine learning solutions on AWS Trainium based systems. Key job responsibilities You will contribute to the design and implementation of distributed training solutions for large-scale ML models running on Trainium instances. A significant part of your work will involve extending and optimizing popular distributed training frameworks including FSDP, torchtitan, and Hugging Face libraries for the Neuron ecosystem. A core focus of this role involves developing and optimizing mixed-precision and low-precision training techniques. You will work with BF16, FP8, and emerging numerical formats to improve training throughput while maintaining model accuracy and convergence quality. This includes implementing precision-aware training strategies, loss scaling techniques, and careful gradient management to ensure training stability across reduced precision formats. Beyond precision optimization, you will profile, analyze, and tune end-to-en