> Markdown version of [/jobs/ext/160816-sr-ml-kernel-performance-engineer-aws-neuron](https://www.wearedevelopers.com/jobs/ext/160816-sr-ml-kernel-performance-engineer-aws-neuron). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. ML Kernel Performance Engineer, AWS Neuron,... - **Company:** Amazon.com, Inc. - **Location:** Cupertino, CA, United States - **Experience:** Expert - **Salary:** $193,300.0 - $261,500.0 - **Contract:** Internship / Graduate position - **Skills:** Artificial Intelligence, Amazon Web Services, Application Frameworks, Code Review, Nvidia CUDA, Computer Programming, Shard (Database Architecture), Software Design Patterns, Microprocessors, Distributed Systems, Field-Programmable Gate Array (FPGA), General-Purpose Computing on Graphics Processing Units, Machine Learning, OpenCL, Performance Tuning, Tensorflow, Software Engineering, Systems Architecture, Graphics Processing Unit (GPU), High Performance Computing, Pytorch, Deep Learning, Parallel Computation, Backend, Information Technology, Optimization Algorithms, Build Process, Machine Learning Operations, Software Coding, Software Version Control, Programming Languages - **Published:** May 16, 2026 - **Apply:** https://www.juju.com/job/00000000fzu472 ## About the Role 5+ years of non-internship professional software development experience - 5+ years of programming with at least one software programming language experience - 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience - 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience - Experience as a mentor, tech lead or leading an engineering team Preferred Qualifications - Bachelor's degree in computer science or equivalent - 6+ years of full software development experience - Expertise in accelerator architectures for ML or HPC such as GPUs, CPUs, FPGAs, or custom architectures - Experience with GPU kernel optimization and GPGPU computing such as CUDA, NKI, Triton, OpenCL, SYCL, or ROCm - Demonstrated experience with NVIDIA PTX and/or AMD GPU ISA - Experience developing high performance libraries for HPC applications - Proficiency in low-level performance optimization for GPUs - Experience with LLVM/MLIR backend development for GPUs - Knowledge of ML frameworks (PyTorch, TensorFlow) and their GPU backends - Experience with parallel programming and optimization techniques - Understanding of GPU memory hierarchies and optimization strategies ## Description 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 Acceleration Kernel Library team is at the forefront of maximizing performance for AWS's custom ML accelerators. Working at the hardware-software boundary, our engineers craft high-performance kernels for ML functions, ensuring every FLOP counts in delivering optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, 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, enabling unparalleled ML inference and training performance. As part of the broader Neuron Compiler organization, our team works across multiple technology layers - from frameworks and compilers to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely 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, where you'll help shape the future of AI acceleration technology This is an opportunity to work on cutting-edge products at the intersection of machine-learning, high-performance computing, and distributed architectures. You will architect and implement business-critical features, publish cutting-edge research, and mentor a brilliant team of experienced engineers. 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., Our kernel 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 and implement high-performance compute kernels for ML operations, leveraging the Neuron architecture and programming models * Analyze and optimize kernel-level performance across multiple generations of Neuron hardware * Conduct detailed performance analysis using profiling tools to identify and resolve bottlenecks * Implement compiler optimizations such as fusion, sharding, tiling, and scheduling * Work directly with customers to enable and optimize their ML models on AWS accelerators * Collaborate across teams to develop innovative kernel optimization techniques ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Accelerating Python on GPUs](https://www.wearedevelopers.com/videos/859-accelerating-python-on-gpus) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [From Model to Metal: An Open Source Stack for Accelerating Intelligence](https://www.wearedevelopers.com/videos/1636-from-model-to-metal-an-open-source-stack-for-accelerating-intelligence) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Coffee with Developers - Stephen Jones - NVIDIA](https://www.wearedevelopers.com/videos/1303-coffee-with-developers-stephen-jones-nvidia) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Top 6 Hackathons for Developers in 2023](https://www.wearedevelopers.com/magazine/263-top-6-hackathons-for-developers-in-2023) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)