We are looking for an experience compiler engineer for an exciting role in the GPU Assembler team. Join the GPU Assembler team and help build tools that enable creation of most performant applications and libraries for HPC, DL and Autonomous Driving domain. Work with key HPC developers around the world to help them achieve the best performance & efficiency on current and next generation NVIDIA GPUs by developing best in industry Assembler and Code Analysis Tools.
We will be part of a team that develops a core component of our platform for accelerating general purpose computation on the GPU. Disassembler (NVDISASM) developed by the team is used by all the CUDA users worldwide to analyze their binaries with various code analysis features built into the disassembler. Assembler is used for achieving the best performance for key DL applications.
What you will be doing:
Lead design and development of NVIDIA’s Assembler and Disassembler for GPU compute.
Work on binary analysis & instrumentation features like call graphs generation, program register usage and patching of GPU binaries
Work with GPU architecture and debugger/profiler development teams to understand their requirements and deliver new features & product improvements.
Collaborate closely with teams developing other related components to ensure compatibility, reliability, and high-quality code generation
Working with customers/partners to collect feedback and drive innovative ideas and features to incorporate into the product
What we need to see:
BS or MS degree in Computer Science, Computer Engineering, or related fields with 5+ years of experience in low-level system SW development and a minimum of 3 years related to assemblers, binary analysis tools, debuggers
Good analytical and C/C++ programming skills
Experience in any one area of compiler development including feature support, code generation and compiler infrastructure
Understanding of Assembly Language / Processor ISA (GPU ISA not required but a plus)
Knowledge of object file formats such as ELF and debugging formats (DWARF).
Ways to stand out from the crowd:
Understanding of debugger / profiler tools / bintools / Linker internals, experience in binary analysis / instrumentation tools like BOLT etc.
Usage of AI tools in everyday work like Cursor, Windsurf etc.
Knowledge of GPU development and compute APIs such as CUDA and OpenCL
Top Skills
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
