Amazon Sr Applied Scientist SSG Science 

India, Karnataka 

251724263

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 techniquesAbout the team
#1. Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

- 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

- 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