GPU​/AI System Technology and Engineering Intern – AI​/HPC

GPU/AI Application System Software Engineer Intern (System Technologies and Engineering) - 2026 Summer (BS/MS) | Tik Tok

This is an internship at Tik Tok, likely located in Los Angeles, CA. Tik Tok is a global destination for short-form mobile video, with a mission to inspire creativity and bring joy. This role contributes to developing optimized operating system and system software for deep learning and high-performance computing workloads in large-scale data centers, delivering core software components for the next generation of AI and HPC platforms.

This work spans the entire hardware/software stack to ensure peak performance for AI and HPC infrastructure.

• Role:
  Internship
• Type:
  Full-time
• Location:
  
  In-person, Los Angeles, CA
• Pay: $45 hourly
• Team: GPU/AI System Technology and Engineering Team
• Mission:
  Develop and optimize OS and system software for deep learning and high-performance computing workloads in large-scale data centers.
• Tech Stack:
  Python, C/C++, Linux, Tensor Flow, PyTorch, CUDA, MPI, NCCL, UCX, NVSHMEM, Git

• Design and implement performance benchmarks and testing methodologies to evaluate system performance.
• Develop benchmark tools and performance optimization of AI workloads specifically tailored for large-scale LLM training and inference, as well as High-Performance Computing (HPC).
• Develop Python scripts to automate the testing of various benchmark tools.
• Collaborate with internal teams to identify system bottleneck, debug and improve performance issues.

• Student pursuing a Bachelor's, Master's, or PhD degree in Computer Engineering, Electrical Engineering, Computer Science or related majors, with a background in GPU/CPU benchmarking and familiarity with ML/DL techniques.
• Hands-on experience with Linux-based systems, exposure to testing automation for various applications, and the ability to work independently to complete projects in a timely manner.
• Proficiency in Python and C/C++, and familiarity with ML/DL frameworks like Tensor Flow or PyTorch.
• Strong background in High Performance Computing, ML Hardware Acceleration (e.g., GPU/TPU/RDMA), or ML for Systems; experience with AI model development, parallel programming (MPI, NCCL, UCX, NVSHMEM), CUDA programming, Linux kernel development, Git workflow, and complex system-level debugging.