Software Development Engineer, AI​/ML, AWS Neuron, Model Inference

Software Development Engineer, AI/ML, AWS Neuron, Model Inference

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 Amazon’s custom machine learning accelerators, Inferentia and Trainium. The Neuron SDK, developed by the Annapurna Labs team, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX, enabling unparalleled ML inference and training performance.

The Inference Enablement and Acceleration team works at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch to the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods, and create high-performance kernels for ML functions. We combine deep hardware knowledge with ML expertise to push the boundaries of what’s possible in AI acceleration.

As part of the broader Neuron organization, our team works across multiple technology layers—from frameworks and kernels to compiler, runtime, and collectives. We optimize current performance, contribute to future architecture designs, and collaborate with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work at the intersection of machine learning, high‑performance computing, and distributed architectures.

In this role, you will architect and implement business‑critical features, mentor experienced engineers, and collaborate closely with customers to provide model enablement and optimization expertise. You will work in a small, agile team, inventing and experimenting to deliver peak performance at scale for customers and developers.

• 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.
• Design and implement high‑performance kernels and features for ML operations, leveraging the Neuron architecture and programming models.
• Analyze and optimize system‑level performance across multiple generations of Neuron hardware.
• Conduct detailed performance analysis using profiling tools to identify and resolve bottlenecks.
• Implement optimizations such as fusion, sharding, tiling, and scheduling.
• Conduct comprehensive testing, including unit and end‑to‑end model testing with continuous deployment and releases through pipelines.
• Work directly with customers to enable and optimize their ML models on AWS accelerators.
• Collaborate across teams to develop innovative optimization techniques.

You will collaborate with a cross‑functional team of applied scientists, system engineers, and product managers to deliver state‑of‑the‑art inference capabilities for Generative AI applications. Your work will involve debugging performance issues, optimizing memory usage, and shaping the future of Neuron’s inference stack across Amazon and the open‑source community. You will design, code, implement automation, and resolve software defects to drive efficiencies in software architecture.

The Inference Enablement and Acceleration team fosters a builder’s culture where experimentation is encouraged and impact is measurable. We emphasize collaboration, technical ownership, and continuous learning, supporting new members with mentorship and thorough, kind code reviews. We celebrate knowledge‑sharing and career growth to empower engineers to tackle complex challenges.

• Bachelor’s degree in computer science or equivalent.
• 5+ years of…