Student Researcher, Infra

About the Team

The Seed Infrastructures team at ByteDance oversees distributed training, reinforcement learning frameworks, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models.

Key Responsibilities

• Contribute to AI compiler optimizations for training and inference workloads.
• Develop and extend MLIR-based compiler passes for graph lowering, optimization, and code generation.
• Optimize model execution on GPU and NPU accelerators, focusing on performance, memory efficiency, and scalability.
• Support model deployment pipelines, including compilation, packaging, and runtime integration.
• Assist with distributed training and inference acceleration, such as parallel execution, communication optimization, and runtime scheduling.
• Benchmark, profile, and analyze performance of large-scale models across different hardware backends.
• Collaborate with researchers and engineers to translate model and system requirements into compiler and runtime improvements.

Requirements & Tech Stack

• Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related technical fields.
• Experience using or developing open source frameworks for LLM inference such as vLLM or SGLang.
• Proficient in at least one deep learning framework (e.g., PyTorch, Megatron, DeepSpeed, JAX), with experience in model inference workflows.
• Understanding of modern computing systems, including hardware, storage, and networking, and how they impact ML workloads.
• Familiarity with compilers or model optimization pipelines (e.g., PyTorch Dynamo), or related model execution workflows.
• Able to commit to working for 12 weeks in 2026.

Preferred Qualifications

• Experience with distributed or large-scale ML systems, including training or inference pipelines and related optimizations (e.g., FSDP, DeepSpeed, Megatron, GSPMD).
• Experience with GPU/TPU/NPU programming and performance optimization, or high-performance computing and communication (e.g., CUDA, Triton, NCCL, RDMA).
• Understanding of AI compiler and model optimization stacks (e.g., torch.fx, PyTorch Dynamo, XLA, MLIR).

Compensation Description (Hourly) - Campus Intern

• Hourly rate range: $55 - $55.
• Interns have day one access to health insurance, life insurance, wellbeing benefits.
• Interns also receive 10 paid holidays per year.
• Paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year).
• May be eligible for housing allowance if not working 100% remote.