jobs in KUAILU SOFTWARE (SINGAPORE) PTE. LTD.

全职 AI Engineer 工作, 薪水 up to SGD 5,000, KUAILU SOFTWARE (SINGAPORE) PTE. LTD. Central Region (Singapore) 公司招聘中 - Ricebowl

AI Engineer

KUAILU SOFTWARE (SINGAPORE) PTE. LTD.

SGD5,000 - SGD5,000 每月

Central Region (Singapore)

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工作地点

  • 164 KALLANG WAY Central Region (Singapore) Singapore

职位描述

岗位职责

Responsibilities

  • Design pre-training technical strategies, including data composition, training hyperparameters (LR schedule, batch size ramp-up, progressive sequence length strategies), etc.
  • Lead pre-training data engineering, including data cleaning (deduplication, PII filtering, quality classification), tokenization, data mixing strategies, and curriculum learning.
  • Configure and optimize large-scale distributed training, including 3D parallelism, GPU memory optimization, and communication optimization.
  • Develop and implement long-context training solutions, including RoPE scaling, NTK-aware interpolation, and YaRN.
  • Monitor and optimize training performance, including loss curve analysis, gradient anomaly detection, training stability, and checkpoint management.
  • Conduct regular intermediate evaluations and adjust data composition and training strategies based on evaluation results.

Responsibilities

  • Bachelor’s degree or above in AI, NLP, Computer Science, Systems, or a related field.
  • Proven end-to-end experience in large language model (LLM) pre-training projects, with hands-on experience pre-training models of 7B+ parameters.
  • Strong expertise in large-scale distributed training frameworks, such as Megatron-LM, DeepSpeed, or FSDP, with hands-on experience training on 64+ GPUs.
  • Strong understanding of pre-training data engineering, including data cleaning, deduplication, quality filtering, tokenization, and data mixture optimization.
  • Familiarity with training monitoring and debugging, including loss analysis, gradient monitoring, and training stability management.
  • Familiarity with long-context training techniques, including RoPE scaling, NTK-aware interpolation, and YaRN.

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