Tech Lead, GPU/ML Compiler Architecture

Advanced Micro Devices, Inc.; San Diego (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

Advanced Micro Devices, Inc. is seeking a strategic Software Engineer Lead focused on compiler optimization for upcoming GPU architectures. You will lead efforts to improve performance of critical applications and benchmarks, and work on cutting-edge LLVM-based compilation technology.

The role requires strong leadership, collaboration across teams, and a deep understanding of GPU execution models. You will engage with ML frameworks and HPC workloads while guiding a talented engineering team.

Qualifications

• Strong object-oriented programming background with C/C++ emphasis.
• Experience writing high-quality, well-documented code with attention to detail.
• Experience with modern concurrent programming and threading APIs.
• Track record of contributions to open source LLVM projects.

Responsibilities

• Improve performance of key applications and benchmarks for AMD GPU architectures.
• Collaborate across teams to optimize compiler and code generation pathways.
• Lead design and development of new compiler technologies within LLVM framework.
• Work with architecture teams to align compiler optimizations with hardware features.
• Contribute to ASIC/board bring-up efforts and debugging of toolchains.
• Mentor and lead junior engineers, driving issue resolution and best practices.
• Engage with ML frameworks and HPC workloads to extend compiler optimizations.

Skills

C/C++

OO programming

Concurrency

Open source contributions

Education

Bachelor's/Master's/PhD in CS/CE/EE

Tools

GitHub

Profilers

Job description

Advanced Micro Devices, Inc. is seeking a strategic Software Engineer Lead focused on compiler optimization for upcoming GPU architectures. You will lead efforts to improve performance of critical applications and benchmarks, and work on cutting-edge LLVM-based compilation technology.

The role requires strong leadership, collaboration across teams, and a deep understanding of GPU execution models. You will engage with ML frameworks and HPC workloads while guiding a talented engineering team.