Responsibilities

Team Intro The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models. We are looking for talented individuals to join us for an internship in 2026. PhD Internships at ByteDance aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. PhD internships at ByteDance provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date). Summer Start Dates: - May 11th, 2026 - May 18th, 2026 - May 26th, 2026 - June 8th, 2026 - June 22nd, 2026 Responsibilities - Research and develop our machine learning systems, including heterogeneous computing architecture, management, scheduling, and monitoring. - Manage cross-layer optimization of system and AI algorithms and hardware for machine learning (GPU, ASIC). - Implement both general purpose training framework features and model specific optimizations (e.g. LLM, diffusions). - Improve efficiency and stability for extremely large scale distributed training jobs.

Qualifications

Minimum Qualifications

• Currently in PhD program in distributed, parallel computing principles and know the recent advances in computing, storage, networking, and hardware technologies.
• Familiar with machine learning algorithms, platforms and frameworks such as PyTorch and Jax.
• Have basic understanding of how GPU and/or ASIC works.
• Expert in at least one or two programmingf languages in Linux environment: C/C++, CUDA, Python.
• Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment.

Preferred Qualifications

The following experiences will be a big plus:

• GPU based high performance computing, RDMA high performance network (MPI, NCCL, ibverbs).
• Distributed training framework optimizations such as DeepSpeed, FSDP, Megatron, GSPMD.
• AI compiler stacks such as torch.fx, XLA and MLIR.
• Large scale data processing and parallel computing.
• Experiences in designing and operating large scale systems in cloud computing or machine learning.
• Experiences in in-depth CUDA programming and performance tuning (cutlass, triton).