jobs in UMELIFE (SINGAPORE) PTE. LTD.

Full Time Large Language Model Pre-training Engineer Jobs, salary up to SGD 5,000 in UMELIFE (SINGAPORE) PTE. LTD. - Ricebowl

Large Language Model Pre-training Engineer

UMELIFE (SINGAPORE) PTE. LTD.

SGD5,000 - SGD5,000 Per Month

Central Region (Singapore)

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Working Location

  • 164 KALLANG WAY Central Region (Singapore) Singapore

Job Description

Responsibilities

Job Responsibilities

  • Pre-training Strategy & Architecture
  • Pre-training Data Engineering
  • Large-scale Distributed Training
  • Long-context Training
  • Training Monitoring & Optimization
  • Evaluation & Iterative Optimization

Job Requirements

  • Bachelor's degree or above in Computer Science, Artificial Intelligence, NLP, Machine Learning, Distributed Systems, or a related field.
  • LLM Pre-training Experience
  • Hands-on experience with complete LLM pre-training projects.
  • Experience participating in the pre-training of 7B+ parameter models.
  • Distributed Training
  • Strong expertise in large-scale distributed training frameworks such as Megatron-LM, DeepSpeed, or FSDP.
  • Practical experience with 64+ GPU training environments.
  • Pre-training Data Engineering
  • Solid understanding of large-scale pre-training data pipelines, including data cleaning, deduplication, quality filtering, tokenization, data mixing, and data quality optimization.
  • Training Monitoring & Debugging
  • Strong ability to analyze training loss, gradients, convergence, and training stability.
  • Experience troubleshooting large-scale distributed training issues.
  • Long-context Training
  • Familiarity with long-context training and extension techniques, including RoPE scaling, NTK-aware interpolation, and YaRN.
  • Preferred Qualifications
  • Experience with 70B+ parameter model pre-training.
  • Experience with MoE (Mixture-of-Experts) model pre-training.
  • Publications in top-tier AI/ML conferences such as NeurIPS, ICML, ICLR, ACL, or EMNLP, particularly in LLM pre-training, model architecture, or training optimization.
  • Experience optimizing large-scale GPU clusters and training infrastructure.

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