Machine Learning Compiler Engineer @ Amazon Web Services (AWS)
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About the Role
The Machine Learning Compiler Engineer at Amazon Web Services (AWS) focuses on the AWS Neuron Compiler. This cutting-edge deep learning compiler powers Generative AI and advanced ML workloads on custom ML accelerators such as Inferentia and Trainium.
Responsibilities
Design and optimize compiler features for high-performance ML workloads. Solve complex challenges in areas like graph optimization, code generation, and hardware-software co-design. Collaborate across Runtime, Frameworks, and Hardware teams to maximize end-to-end performance.
Team & Environment
Join the Backend Compiler Team to work on instruction scheduling, memory management, parallelism, graph partitioning, ISA design, and new hardware bring-up. Enjoy a startup-like development environment with hands-on involvement in design discussions, code reviews, and cross-functional collaboration.
Day-to-Day
Design and code innovative software solutions. Develop metrics, implement automation, and resolve software defects. Engage in design discussions and collaborate with internal and external stakeholders to drive technical decisions.
Why AWS?
AWS pioneers cloud computing, delivering robust products to startups and Global 500 companies. Embrace work-life balance with flexible hours and a culture of inclusivity, mentorship, and career growth.
Key Skills/Competency
- Compiler Optimization
- Graph Optimization
- Code Generation
- Hardware-Software Co-design
- Instruction Scheduling
- Memory Management
- Parallelism
- LLVM/MLIR
- FPGA Algorithms
- Deep Learning Frameworks
How to Get Hired at Amazon Web Services (AWS)
🎯 Tips for Getting Hired
- Customize your resume: Highlight ML compiler and systems skills.
- Study AWS culture: Research values and tech innovations at Amazon Web Services.
- Show technical depth: Emphasize compiler optimization and graph theory experience.
- Prepare for cross-functional interviews: Be ready for technical and behavioral questions.