GPU Software Engineer
Role details
Job location
Tech stack
Job description
We are looking for a GPU application engineer to enable AMD GPU acceleration through HIP for applications that currently use CUDA backends, or that require a new HIP-based backend.
This role is part of an ISV enablement engineering team and focuses on application-level GPU programming, performance tuning, and integration., * Port CUDA-based GPU code to HIP, including kernels, runtime logic, and build systems
- Design and implement HIP backends for applications without existing AMD GPU support
- Enable and integrate HIP ray tracing (HIPRT) where applicable
- Optimize GPU kernels and application pipelines for AMD GPU architectures
- Debug correctness, performance, and stability issues in application-level GPU code
- Work with profiling tools to identify and resolve bottlenecks
- Collaborate with internal compiler/runtime teams to escalate lower-level issues when needed
- Maintain clean, portable GPU code across vendors, __AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here.
Requirements
The ideal candidate is a highly self-driven GPU application engineer who can be productive from day one within a complex, performance-critical codebase. You have hands-on experience developing and optimizing mature CUDA-based applications, and you bring a pragmatic, engineering-first mindset: knowing when to chase peak performance and when to favor clarity, portability, and long-term maintainability. You work independently, stay organized while driving multiple parallel efforts, and communicate clearly with cross-functional teams, application developers, and stakeholders., * Advanced software development experience, with significant experience in GPU programming
- Strong C/C++ programming skills
- Hands-on experience with CUDA (kernel development, memory management, streams/events)
- Experience developing GPU-accelerated applications
- Solid understanding of GPU programming fundamentals
- Strong debugging skills for GPU kernels and application pipelines
- Experience developing on Linux
- Experience with HIP and ROCm
- Experience porting CUDA code to HIP (manual or hipify-based)
- Familiarity with HIPRT or GPU ray tracing concepts
- Experience with GPU profiling tools
- Experience working in large, multi-backend codebases
- Familiarity with NVIDIA OptiX or similar GPU ray tracing APIs
Academic Credentials
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent
