The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on AWS Trainium, Amazon's custom machine learning accelerator. Neuron includes an ML compiler, runtime, collectives library, and application framework that integrate with PyTorch and JAX, so customers can train frontier-scale models on Trainium without rewriting their stack.
The Distributed Training team enables the training of a wide range of models, from large-scale pretraining through post-training and reinforcement learning, on AWS's custom ML accelerators. As more customer workloads shift toward RLHF, PPO/GRPO, and other fine-tuning methods, we are building the distributed training infrastructure, parallelism techniques, numerics, and high-performance kernels that these methods depend on. As part of the broader Neuron organization, we work across frameworks, kernels, compiler, runtime, and collectives — a true hardware and software co-design in practice. We not only optimize current performance but also contribute to future architecture designs, since the gaps we characterize today become requirements for the next generation of Trainium.
We are looking for passionate technical leaders who can help us build and fine tune these distributed training solutions. This role offers a rare opportunity to work at the intersection of machine learning, high-performance computing, and distributed systems, where you will help shape the direction of AI acceleration technology.
Key job responsibilities
You will lead the effort to build distributed training and post-training support into PyTorch and JAX for Trainium accelerators. You will work across PyTorch and Neuron software stack with the Neuron compiler and runtime teams to enable and fine tune large-scale training, post training, and reinforcement learning workloads on the latest Trainium instances. You will own the parallelism strategies these models depend on, spanning data, tensor, pipeline, expert, and context parallelism, and apply reduced-precision formats where they measurably pay off. You will profile end to end to determine whether a workload is bound by compute, memory, collectives, or host overhead, then drive the fix to the layer that owns it, working with compiler, runtime, and collectives engineers to land it. You will translate the performance gaps you characterize into requirements that influence frameworks, and contribute upstream to the open source frameworks our customers train on.
About the team
Inclusive Team Culture
Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
Work/Life Balance
Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.