Software Engineer II - AI​/ML, Neuron Inference, Inference

The Annapurna Labs team at Amazon Web Services (AWS) builds the AWS Neuron SDK, the software development kit used to accelerate deep learning and GenAI workloads on AWS Trainium machine learning accelerators.

The Neuron Inference organization is at the forefront of optimizing inference performance for a wide range of ML models and model architectures on AWS's custom ML accelerators. We are working across the stack from PyTorch to the hardware-software boundary, our engineers build systematic infrastructure, develop high-performance kernels for ML functions, ensuring every compute unit is fine-tuned for optimal performance forour customers' demanding workloads.

We combine deep hardware knowledge with ML expertise to push the boundaries of what'spossible in AI acceleration.

As part of the broader Neuron organization, our team works across multiple technology layers — from frameworks and kernels through to compiler, runtime, and collectives. We not only optimize performance on open weight models but also contribute to future architecture designs and work closely with customers to enable optimal performance on their models.

This role offers a unique opportunity to work at the intersection of machine learning, high-performance computing, and distributed architectures, where you'll help shape the future of AI acceleration technology.

As a Software Engineer II - AI/ML, you will design and implement business-critical features and own the performance of models on AWS Trainium end to end. We operate in spaces that are very large, yet our teams remain small and agile. There is no blueprint. We're inventing. We're experimenting. It is a very unique learning culture. The team works closely with customers on their model enablement, providing direct support and optimization expertise to ensure their machine learning workloads achieve optimal performance on AWS ML accelerators.

The team also collaborates with open source ecosystems to provide seamless integration and bring peak performance at scale for customers and developers.

This role is responsible for development, enablement, and performance tuning of a wide variety of LLM model families, including massive-scale large language models like the GLM, GPT-OSS, Kimi, and beyond. The Model Enablement team works side by side with compiler engineers and runtime engineers to create, build, and tune distributed inference solutions with AWS Trainium.

You can learn more about Neuron:
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Are you passionate about building software that operates at massive scale and directly impacts millions of customers? As a Software Development Engineer II at Amazon, you will design, build, and maintain systems that solve complex problems while delivering meaningful results. You will contribute across the full software development lifecycle — from working backwards from customer needs to designing, implementing, testing, deploying, and operating production software.

This role offers the opportunity to tackle challenging technical problems, collaborate with talented engineers, and grow your skills while making a real impact. If you are a curious, self-motivated engineer who thrives on turning ambiguity into clarity and shipping high-quality code, we want to hear from you.

Key job responsibilities

This role contributes to building distributed inference support for PyTorch in the Neuron SDK, and tunes models to ensure the highest performance and maximize efficiency running on AWS Trainium. Strong software development in Python, system-level programming, and ML knowledge are all critical to this role. Our engineers collaborate across compiler, runtime, framework, and hardware teams to optimize machine learning workloads for our global customer base.

Working at the intersection of software, hardware, and machine learning systems, you'll bring expertise in low-level optimization, system architecture, and ML model acceleration. In this role, you will:
* Design, develop, and optimize machine learning models and frameworks for deployment on custom ML hardware accelerators.
* Participate in all stages of the ML system development lifecycle, including distributed-computing-based architecture design, implementation, performance profiling, hardware-specific optimizations, testing, and production deployment.
* Build infrastructure to systematically analyze and onboard multiple models with diverse architectures.
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