Amazon ML Compiler Engineer AWS Neuron Annapurna Labs 

Canada, Ontario, Old Toronto 

647437071

Key job responsibilities
Our performance engineers collaborate across compiler, runtime, and framework teams to optimize machine learning workloads for our global customer base. Working at the intersection of machine learning, high-performance computing, and distributed systems, you'll bring a passion for performance analysis, distributed systems, and machine learning. In this role, you will:- Analyze and optimize system-level performance of machine learning models across the entire technology stack, from frameworks to runtime
- Conduct detailed performance analysis and profiling of ML workloads, identifying and resolving bottlenecks in large-scale ML systems
- Work directly with customers to enable and optimize their ML models on AWS accelerators, understanding their specific requirements and use cases
- Design and implement compiler optimizations, transforming manual performance improvements into automated compiler passes- Work in a startup-like development environment, where you’re always working on the most important stuff.About 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.

- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language

- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
- Experience in compiler design for CPU/GPU/Vector engines/ML-accelerators.
- Experience with System Level performance analysis and optimization
- Experience with LLVM and/or MLIR
- Experience with the following technologies: PyTorch, OpenXLA, StableHLO, JAX, TVM, deep learning models, and algorithms.