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Research Engineer

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Research Engineer in USA new

  • ByteDance
  • Full time
  • Email
  • San Jose, CA

Responsibilities

  • Conduct research and engineering development on large-scale infrastructure for efficient pretraining of foundation models, multimodal LLMs, and video generation models.
  • Design and optimize distributed training strategies, including data, pipeline, and expert parallelism, computation and communication optimization, and throughput scaling on large GPU clusters.
  • Analyze performance bottlenecks in exascale training systems and develop data-driven optimization methods.
  • Translate research ideas into scalable, real-world production infrastructure solutions.

Requirements

  • Bachelor’s degree or above in Computer Science, Software Engineering, or a related field.
  • At least 2 years of experience in large-scale model training, distributed training systems, or GPU kernel optimization.
  • Strong understanding of large-scale LLM and multimodal model training.
  • Experience with parallelism strategies on large GPU clusters or GPU kernel performance optimization.
  • Strong understanding of algorithm–system co-design and cross-layer optimization for training efficiency, scalability, and reliability.
  • Strong programming skills and hands-on experience implementing production-grade ML systems or infrastructure.
  • Preferred: experience pretraining LLM, multimodal, or video generation models at scale; using Megatron-LM, DeepSpeed, PyTorch FSDP, or NCCL; or developing custom kernels with CUDA or Triton.
  • Preferred: publications at top-tier machine learning or systems conferences or notable open-source contributions to widely used training frameworks.

ByteDance

ByteDance is a global incubator of platforms at the cutting edge of commerce, content, entertainment and enterprise services - over 2.5bn people interact with ByteDance products including TikTok. Creation is the core of ByteDance's purpose. Our products are built to help imaginations thrive. This...

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