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2027 PhD AI Training Systems and Performance Engineer Intern/Co-Op

AMD
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Hybrid (San Jose, CA)$91.2kUNAVAILABLEPosted 2w ago

About the role

Intern/Co-Op position at AMD focused on AI training systems, performance engineering, and optimization on AMD Instinct GPUs. Role involves bringing up large-scale training workloads, profiling, developing automation tools, and exploring optimization opportunities in multi-GPU environments. Onsite, full-time during co-op/intern terms in San Jose, CA or Santa Clara, CA.

Responsibilities

  • Develop and optimize large-scale AI training and fine-tuning workloads on AMD GPU platforms.
  • Bring up newly released foundation models and training frameworks, adapting implementations and training recipes for AMD hardware while establishing reproducible correctness and performance baselines.
  • Profile and analyze AI workloads to identify bottlenecks across GPUs, CPUs, memory systems, networking, and communication infrastructure.
  • Develop tools/workflows to automate training setup, debugging, performance analysis, and optimization using LLM-powered agents and agentic AI techniques.
  • Investigate and implement optimization strategies to improve training throughput, GPU utilization, memory efficiency, and scalability across distributed multi-GPU environments.
  • Expose advanced profiling and performance analysis tools to benchmark and tune AI frameworks, libraries, SDKs, and applications on AMD platforms.
  • Collaborate with software engineers and architects to evaluate emerging AI models, distributed training techniques, and performance optimization opportunities.
  • Transform experiments into reusable workflows, best practices, and software improvements for out-of-the-box AI training performance on AMD hardware.

Qualifications

  • Currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Machine Learning, or related technical field.
  • Strong programming experience in Python and/or C++.
  • Hands-on experience implementing, training, and debugging deep learning models using PyTorch, JAX, TensorFlow, vLLM, or SGLang.
  • Experience in distributed training systems, data/tensor/pipeline/expert/context parallelism, GPU performance optimization, AI systems software, HPC, large language model training and fine-tuning, or agentic AI/LLM-powered automation.
  • Understanding of transformer-based architectures, mixture-of-experts models, and modern LLM training techniques.
  • Experience profiling workloads with tools such as PyTorch Profiler, ROCm Profiler, VTune, Nsight, or similar.
  • Familiarity with MPI, NCCL/RCCL, OpenMP or related distributed training technologies.
  • Understanding of GPU architecture, memory systems, and performance tuning; ability to identify and resolve bottlenecks in large-scale AI workloads.
  • Experience with ROCm, HIP, Triton, GPU kernel optimization, or AI systems software development is a plus.
  • Publications in relevant fields are a plus.

Skills

Required:PythonC++Deep learning frameworksDistributed trainingProfiling toolsTransformer/LLM conceptsGPU optimization
Last updated Oct 22, 2026. Interested? Apply on the company site.
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