2027 PhD HPC & Sovereign AI Center of Excellence Intern/Co-op
About The Position
As an AMD intern and co-op, you’ll be placed at the epicenter of the AI ecosystem, working alongside experts and industry pioneers. You’ll do important work, learn new skills, expand your network, and gain real-world experience on projects that impact millions of end-users worldwide. Whether you’re an undergrad or a PhD student, your contributions matter—and your experience here will be a launchpad for what comes next. We are seeking a motivated PhD intern to join our research team working at the intersection of High-Performance Computing (HPC) and Artificial Intelligence (AI). In this role, you will contribute to cutting-edge projects aimed at optimizing AI workloads for large-scale computing environments, developing novel algorithms, or exploring the integration of AI techniques to enhance HPC applications.
Requirements
- Currently enrolled in a PhD program in Computer Science, Computational Science, Electrical/Computer Engineering, Applied Mathematics, or a related field.
- Strong background in parallel computing, distributed systems, or AI/ML frameworks.
- Proficiency in programming languages such as Python, C/C++, Fortrans or ROCm/CUDA.
- Experience with at least one deep learning framework (e.g., PyTorch, TensorFlow, JAX).
- Familiarity with MPI, OpenMP, or GPU programming.
- Solid understanding of numerical methods, optimization, or scientific computing.
- Experience with performance analysis, hot-spot identification
- Experience developing GPU kernels, analyzing and quantifying benefits of GPU offloading.
Responsibilities
- Build, run, and analyze performance of benchmarks and application on GPU accelerated platforms.
- Assess the capabilities of development tools and runtime environment in terms of capabilities, performance, and usability.
- Explore the benefits of different code optimization techniques
- Optimize communication patterns for HPC applications
- Investigate how GPU development tools can be adapted to future AMD GPUs
- Identify and measure performance capabilities across GPU families.
- Agents “ranger” through Jira/Atlassian to harvest reproducers.
- Use LLM workflows (maybe eventually agents) and existing Atlassian/Jira/Internal AMD AI available tools to incorporate existing bug reproducers
- Perform a thorough study porting simple synthetic workloads and maybe proxy apps to adaptiveCPP (SYCL) and measuring performance.
Benefits
- AMD benefits at a glance.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Ph.D. or professional degree
