jobapplier

Research Engineer (Seed Model - Infra - Training Performance & Core Kernels)

ByteDance

Skills

Required

  • GPU architecture
  • Software Engineering
  • C/C++
  • Python
  • algorithms
  • data structures
  • systems programming
  • containerization
  • server-side
  • debugging
  • machine learning framework
  • PyTorch
  • TensorFlow

Preferred

  • LLM
  • infrastructure
  • CUDA / OpenCL
  • optimization
  • LLM deployment
  • GPU programming
  • TensorRT / Triton / CUTLASS

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

Responsibilities

  • Design, develop, and optimize high-performance inference systems for large-scale LLMs and VLMs, covering inference engines, serving frameworks, and end-to-end deployment pipelines.
  • Build state-of-the-art model inference engines through advanced performance optimization techniques such as compiler-level optimizations, parallel computing, graph fusion, efficient CUDA kernel development, low-precision computation, streaming inference, speculative decoding, and high-concurrency request optimization.
  • Collaborate closely with other research teams to identify performance bottlenecks, conduct in-depth performance analysis, and optimize large models; contribute to the development of model toolchains and the broader technical ecosystem.

Requirements

  • Bachelor's degree or above in Computer Science, Electrical Engineering, Software Engineering, or a related field.
  • Strong proficiency in C/C++ and Python; solid foundations in algorithms, data structures, and systems programming; familiarity with containerization and server-side debugging.
  • Hands-on experience with at least one mainstream machine learning framework (e.g., PyTorch, TensorFlow).
  • Experience deploying or optimizing LLM/VLM inference at production scale, with demonstrated impact on latency, throughput, or serving cost.
  • Familiarity with GPU architecture and experience optimizing compute-intensive operators (e.g., FlashAttention, GEMM, GEMV, Conv2D).

Preferred

  • Experience with large-scale LLM serving infrastructure or equivalent production LLM deployment experience.
  • Experience in GPU programming (CUDA/OpenCL) and familiarity with frameworks such as TensorRT, Triton, or CUTLASS.
  • Experience in performance modeling, profiling, and optimization, or strong knowledge of CPU/GPU architectures.
  • Familiarity with model/data parallelism frameworks for distributed inference.

Every job at ByteDance