Shape the Future of AI Accelerators at AWS Neuron
We build Amazon Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium.
As a Senior Software Engineer on our Machine Learning Applications team, you will optimize the world's most demanding AI models at a scale few engineers ever get to work on. 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.
You can learn more about Neuron
https://awsdocs-neuron.readthedocs-hosted.com
https://aws.amazon.com/machine-learning/neuron/ https://github.com/aws/aws-neuron-sdk https://www.amazon.science/how-silicon-innovation-became-the-secret-sauce-behind-awss-success
Key job responsibilities
Key job responsibilities
This role will help lead the efforts in building distributed inference support for Pytorch in the Neuron SDK. This role will tune these models to ensure highest performance and maximize the efficiency of them running on the customer AWS Trainium and Inferentia silicon and servers. Strong software development using Python, System level programming and ML knowledge are both 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 including GPT, Kimi, and Qwen on custom AI accelerators.
* Participate in all stages of the ML system development lifecycle including distributed computing based architecture design, implementation, performance profiling, low level optimizations, and production deployment.
* Build infrastructure to systematically analyze and onboard multiple models with diverse architecture.
* 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
* Work directly with customers to enable and optimize their ML models on AWS accelerators
* Collaborate across teams to develop innovative optimization techniques
• Develop kernels to improve model efficiency on Amazon AI Accelerators
• Transform complex tensor operations into highly optimized graph implementations
• Optimize state-of-the-art language, vision, and multimodal generative AI models for Neuron hardware
What Makes This Role Unique:
• Direct influence on AWS's AI Accelerator used by thousands of ML applications
• Full-stack optimization from high-level frameworks to low level primitives
• Collaboration with both open-source ML communities and hardware architecture teams
• Requires passion for performance tuning and system architecture
A day in the life
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. As you design and code solutions to help our team drive efficiencies in software architecture, you'll create metrics, implement automation and other improvements, and resolve the root cause of software defects.
You will also build high-impact solutions to deliver to our large customer base and participate in design discussions, code review, and communicate with internal and external stakeholders. You will work cross-functionally to help drive business decisions with your technical input. You will work in a startup-like development environment, where you're always working on the most important initiative.
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.
About the team
At AWS Neuron, we're revolutionizing how the world's most sophisticated AI models run at scale through Amazon's next-generation AI accelerators.
The Inference Enablement and Acceleration team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for Amazon's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic software stack, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. The team collaborates with open source ecosystems to provide seamless integration and bring peak performance at scale for customers and developers.
BASIC QUALIFICATIONS
- Bachelor's degree
- 5+ years of non-internship professional software development experience
- Knowledge of Python and/or C++ programming
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience in debugging, profiling, and implementing software engineering best practices in large-scale systems
- Knowledge of system performance, memory management, and parallel computing principles
- Experience owning a performance optimization roadmap and mentoring engineers on optimization
PREFERRED QUALIFICATIONS
- Master's degree in computer science or equivalent
- Knowledge of machine learning model architecture and inference
- Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques
- Hands-on development with PyTorch is preferred
- Experience scaling workloads across multi-GPU and multi-node topologies with NCCL and tensor, pipeline, or expert parallelism
- Experience writing and optimizing custom CUDA/Triton kernels for tensor operations
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 168,100.00 - 227,400.00 USD annually
