Senior GPU Compiler Development Engineer

Job Title

Senior GPU Compiler Development Engineer

Role Summary

Join the GPU Assembler team to design and implement assembler, disassembler, and binary-analysis tools used by GPU compute developers. The role focuses on enabling high-performance applications for HPC, deep learning, and autonomous systems by developing assembler/disassembler features, binary instrumentation, and code-analysis tools.

The team delivers core platform components, collaborates with GPU architecture and debugger/profiler teams, and works with external customers to maximize performance and reliability.

Experience Level

Senior — typically requires 5+ years of relevant industry experience with at least 3 years working on assemblers, binary analysis tools, or debuggers.

Responsibilities

Primary responsibilities include design, implementation, and cross-team collaboration:

• Lead design and development of assembler and disassembler components for GPU compute.
• Develop binary analysis and instrumentation features such as call-graph generation, register-usage analysis, and binary patching.
• Collaborate with GPU architecture and debugger/profiler teams to define and deliver new features.
• Work with related component teams to ensure compatibility and high-quality code generation.
• Engage with customers and partners to collect feedback and incorporate product improvements.

Requirements

Must-have skills and experience:

• 5+ years of low-level system software development experience, with a minimum of 3 years specifically on assemblers, binary-analysis tools, or debuggers.
• Strong C/C++ programming and analytical skills.
• Experience in compiler development areas such as feature support, code generation, or compiler infrastructure.
• Understanding of assembly language and processor ISA concepts (GPU ISA experience is a plus).
• Knowledge of object file formats (e.g., ELF) and debugging formats (e.g., DWARF).

Nice-to-have:

• Understanding of debugger/profiler tools, bintools, linker internals, or experience with binary-instrumentation tools (e.g., BOLT).
• Familiarity with GPU development and compute APIs such as CUDA or OpenCL.
• Practical use of AI-assisted developer tools.

Education Requirements

BS or MS in Computer Science, Computer Engineering, or a related technical field; or equivalent practical experience.