below marketmost similar roles pay hereabove market
This role pays less than
71%
of similar roles. Most pay
$114,400–$218,500
— the blue band above.
At the midpoint, this role pays about
$114k
versus about
$166k
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
510 open roles
on FindRole.
Listed pay typically runs
$172,000–$258,000
across 510 roles with salary data.
The 2027 PhD AI Training Systems and Performance Engineer Intern/Co-Op joins a team of software engineers, architects, and AI specialists to accelerate the adoption and optimization of cutting-edge AI training workloads on AMD Instinct GPUs. The intern will bring up new training workloads, analyze performance bottlenecks, and develop innovative tooling to improve scalability, efficiency, and developer productivity. Key responsibilities include optimizing large-scale training and fine-tuning workloads, profiling bottlenecks across GPUs, CPUs, and networking, and implementing optimization strategies for distributed multi-GPU environments. Required skills include Python and C++ programming, experience with PyTorch, JAX, TensorFlow, vLLM, or SGLang, and knowledge of distributed training, GPU performance optimization, and transformer-based architectures. The role focuses on improving training throughput, GPU utilization, and memory efficiency for foundation models and large language models.
Develop and optimize large-scale AI training and fine-tuning workloads on AMD GPU platforms.
Bring up newly released foundation models and training frameworks on AMD hardware.
Profile and analyze AI workloads to identify bottlenecks across GPUs, CPUs, memory, and networking.
Develop tools and workflows to automate training setup and debugging using LLM-powered agents.
Implement optimization strategies to improve training throughput, GPU utilization, and memory efficiency.
Benchmark and tune AI frameworks, libraries, and SDKs using advanced profiling tools.
Transform successful experiments into reusable workflows and software improvements for out-of-the-box performance.
What we're looking for
Currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Machine Learning, or a related technical discipline.
Strong programming experience in Python and/or C++.
Hands-on experience implementing, training, and debugging deep learning models using frameworks such as PyTorch, JAX, TensorFlow, vLLM, or SGLang.
Experience with distributed training systems, GPU performance optimization, HPC, or large language model training and fine-tuning.
Understanding of transformer-based architectures, mixture-of-experts models, and modern LLM training techniques.
Experience profiling workloads using tools such as PyTorch Profiler, ROCm Profiler, VTune, Nsight, or similar tools.
Familiarity with distributed training technologies and communication libraries such as MPI, NCCL/RCCL, OpenMP, or related frameworks.
Experience identifying and resolving compute, memory, data-loading, or communication bottlenecks in large-scale AI workloads (preferred).
Python
C/C++
PyTorch
TensorFlow
JAX
CUDA
HIP
OpenCL
Triton
ONNX Runtime
vLLM
SGLang
Docker
CI/CD
Git
CMake
Make
Conda
Distributed Systems
MLOps
Quantization
Kernel Development