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 AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's 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 Training Enablement and Foundation team is 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 till the hardware-software boundary, our engineers build systematic infrastructure, 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. 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 and collaborate with compiler 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
You will architect and implement business critical features, 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 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 Llama family, DeepSeek and beyond.
Key job responsibilities This role will help lead the efforts in building distributed training support for Pytorch/JAX in the Neuron SDK. This role will enable frameworks, tune models to ensure highest performance and maximize the efficiency of them running on the customer AWS Trainium silicon and servers. Strong software development using Python, C++, 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 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 architecture.
- Analyze and optimize system-level performance across multiple generations of Neuron hardware
- Conduct detailed performance analysis using profiling tools to identify and resolve bottlenecks
- 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
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.
About the team Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. Join us to solve some of the most interesting and impactful infrastructure challenges in AI/ML today.
Basic Qualifications
- 3+ years of non-internship professional software development experience, or Bachelor's degree or above in engineering or equivalent
- 3+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
- Knowledge of system performance, memory management, and parallel computing principles
- Experience in debugging, profiling, and implementing software engineering best practices in large-scale systems, or experience debugging, profiling, and implementing best software engineering practices in large-scale systems
Preferred Qualifications
- Experience with PyTorch, JIT compilation, and AOT tracing, or experience programming with at least one software programming language
- Experience with CUDA kernels or ML/low-level kernels, or experience in computer architecture
- Experience with distributed training at scale (FSDP, Tensor Parallelism, Expert Parallelism)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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 - 143,700.00 - 194,400.00 USD annually