PhD HPC & Sovereign AI Center of Excellence Intern/Co-op

Amd

Confirmed live yesterday High trust

Hybrid

Quick summary

Work type: Hybrid
Location: San Jose, CASanta Clara, CA
Salary: $91,520–$137,280 / yr
Employment: Intern
Posted: 2 days ago
Freshness: Confirmed live yesterday
Closes: Sep 18, 2027
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $184k

This role $114k

$73k most similar roles pay here $260k

This role pays less than 77% of similar roles. Most pay $126,350–$241,937 — the shaded band above. At the midpoint, this role pays about $114k versus about $184k for comparable roles.

Based on 240 similar postings.

Employer

About Amd

AMD (Advanced Micro Devices) is a semiconductor company that develops high-performance processors, graphics cards, and adaptive computing solutions for gaming, data centers, and embedded markets. Industry: Semiconductors

Amd currently has 391 open roles on FindRole.

Listed pay typically runs $164,080–$246,120 across 391 roles with salary data.

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At a glance

TL;DR · PhD HPC & Sovereign AI Center of Excellence Intern/Co-op

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The 2027 PhD HPC & Sovereign AI Center of Excellence Intern/Co-op joins the research team to work at the intersection of High-Performance Computing and Artificial Intelligence. The role involves optimizing AI workloads for large-scale computing environments, developing novel algorithms, and exploring AI techniques to enhance HPC applications. Day-to-day responsibilities include building and analyzing performance benchmarks on GPU accelerated platforms, assessing development tools and runtime environments, and investigating code optimization techniques. Specific projects involve optimizing communication patterns, adapting GPU development tools, and porting workloads to adaptiveCPP (SYCL). Candidates should possess expertise in parallel computing, distributed systems, or AI/ML frameworks. Required skills include proficiency in Python, C/C++, Fortran, ROCm, or CUDA, along with experience in PyTorch, TensorFlow, JAX, MPI, OpenMP, and GPU kernel development to solve complex performance and optimization challenges in high-performance environments.

Skills

HPC Artificial Intelligence GPU Python C/C++ Fortran ROCm CUDA PyTorch TensorFlow JAX MPI OpenMP SYCL adaptiveCPP LLM Performance Analysis Distributed Systems

What you'll do

Build, run, and analyze the performance of benchmarks and applications on GPU accelerated platforms.
Assess the capabilities, performance, and usability of development tools and runtime environments.
Explore and evaluate various code optimization techniques for high-performance computing.
Optimize communication patterns specifically for HPC applications.
Investigate how GPU development tools can be adapted to future AMD GPU architectures.
Identify and measure performance capabilities across different GPU families.
Use LLM workflows and internal AI tools to harvest bug reproducers from Jira/Atlassian.
Port synthetic workloads and proxy apps to adaptiveCPP (SYCL) while measuring performance.

What we're looking for

Must be 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++, Fortran, or ROCm/CUDA.
Experience with at least one deep learning framework like PyTorch, TensorFlow, or JAX.
Familiarity with MPI, OpenMP, or GPU programming.
Solid understanding of numerical methods, optimization, or scientific computing.
Experience with performance analysis and hot-spot identification.
Experience developing GPU kernels and analyzing the benefits of GPU offloading.

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