This role pays less than
78%
of similar roles. Most pay
$130,425–$241,750
— the shaded band above.
At the midpoint, this role pays about
$114k
versus about
$186k
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.
The 2027 PhD AI Model Optimization & Software Engineer Intern/Co-op joins the team to advance next-generation AI software capabilities. This role involves developing, benchmarking, and optimizing AI software for training, fine-tuning, and inference across CPU, GPU, and accelerator platforms. Responsibilities include profiling workloads to identify bottlenecks, designing kernels using HIP, CUDA, OpenCL, or Triton, and implementing optimization methods like quantization, pruning, and distillation. The candidate will contribute to frameworks such as PyTorch, TensorFlow, JAX, ONNX Runtime, vLLM, or SGLang while exploring compiler technologies and distributed computing systems. Required skills include proficiency in Python and C/C++, along with experience in MLOps, CI/CD pipelines, and containerized environments. The work focuses on solving technical challenges in AI software infrastructure, performance analysis, and the efficiency of large-scale model deployment across diverse hardware architectures.
Develop, benchmark, and optimize AI software for training, fine-tuning, and inference across CPU, GPU, and accelerator platforms.
Profile AI workloads to identify hardware/software bottlenecks and implement performance improvements across compute and memory layers.
Design and optimize GPU or CPU kernels using technologies like HIP, CUDA, OpenCL, or Triton.
Implement optimization methods such as quantization, low-precision inference, sparsity, pruning, and distillation.
Contribute to AI frameworks, libraries, and deployment technologies including PyTorch, TensorFlow, JAX, and vLLM.
Develop parallel and distributed computing methods for scalable training and inference techniques.
Explore compiler, graph optimization, and kernel-generation technologies to improve the efficiency of AI workloads.
Build automated evaluation systems, CI/CD pipelines, and containerized development environments for AI workflows.
What we're looking for
Must be currently enrolled in a U.S.-based PhD program in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, Electrical Engineering, or a related technical field.
Programming experience in Python and/or C/C++.
Experience with one or more AI frameworks or runtimes such as PyTorch, TensorFlow, JAX, ONNX Runtime, vLLM, or SGLang.
Experience, coursework, research, or project work in areas including AI model optimization, GPU kernel development, distributed systems, or MLOps.
Familiarity with software development tools such as Git, CMake, Make, Conda, Docker, or related build technologies.
Research publications, preprints, open-source contributions, or participation in the AI and machine learning developer community (preferred).
PyTorch
TensorFlow
JAX
ONNX Runtime
vLLM
SGLang
CUDA
HIP
OpenCL
Triton
Python
C++
Docker
CI/CD
Git
CMake
Make
Conda
Quantization
Distributed Systems
MLOps
C++
Python
Java
JavaScript
Linux
Unix
MySQL
PostgreSQL
MongoDB
Azure
Django
Rails
Spring Boot
HTML
XML
XSL
Git
GitHub
Perforce
OAuth
SSL
TLS
UML
Embedded Systems
C/C++
Python
Java
JavaScript
Linux
Unix
MySQL
PostgreSQL
MongoDB
Azure
Django
Rails
Spring Boot
HTML
XML
XSL
Git
GitHub
Perforce
OAuth
SSL
TLS
UML
Embedded Systems