About the job
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. 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.
Responsibilities
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
Qualifications
Minimum
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
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