Senior Associate Machine Learning Engineer

Annapurna Labs Inc.

United States

United States

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About

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. 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. Key job responsibilities 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 About the team 1. Diverse Experiences AWS values diverse experiences. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. 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. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future. BASIC QUALIFICATIONS - 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 Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. 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
for more information. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. Applicants should apply via our internal or external career site.

United States

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