Software Engineer- AI/ML, Amazon Neuron Training

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 is at the forefront of training a wide range of models on AWS's custom ML accelerators, supporting novel architectures while maximizing their training performance. Working across the stack from PyTorch and JAX down to the hardware and software boundary, our engineers build the infrastructure that large-scale training depends on, develop new parallelism and numerics techniques, and tune high-performance kernels for the operations that dominate a training step, so every compute unit is doing useful work on our customers' most demanding workloads. We combine deep hardware knowledge with ML expertise to push the limits of training efficiency at scale.

As part of the broader Neuron organization, our team works across multiple technology layers, from frameworks and kernels through to the compiler, runtime, and collectives teams. This is hardware and software co-design in practice. A single throughput gap rarely sits in one layer, so tracing it means following the problem across the stack, deciding where the fix belongs, and working with the team that owns that layer to land it. 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 work closely with customers to enable their models and ensure they train efficiently. 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.

• Help lead the effort to optimize distributed training performance on Trainium, with a primary focus on training throughput across the Neuron software stack.

• Work across PyTorch and Neuron software stack with Neuron compiler and runtime teams to enable and tune large-scale training workloads on the latest Trainium instances.

• Own parallelism strategies spanning data, tensor, pipeline, expert, and context parallelism, and apply reduced-precision formats where they measurably pay off.

• 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.

• Translate performance gaps characterized into requirements that influence frameworks and contribute upstream to open source frameworks.