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Open nowPosted 20 days ago

Large Language Model Pre-training Engineer

MyCareersFuture94,028 open roles

Pay
SGD 5,000 – SGD 10,000 a month
Where
Central, Singapore
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Your applicationOpen nowLarge Language Model Pre-training EngineerMyCareersFuture · Central, Singapore

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Share of postings closed within
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  2. 3.3%3 days
  3. 7.7%7 days
  4. 14.0%14 days
  5. 33.7%30 days
This job: posted 20 days ago

The posting

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, Natural Language Processing (NLP), Machine Learning, Distributed Systems, or a related technical field.
  • Hands-on experience delivering or contributing to end-to-end LLM pre-training projects.
  • Proven experience participating in the pre-training of models with 7B+ parameters.
  • Strong hands-on expertise with distributed training frameworks such as Megatron-LM, DeepSpeed, or FSDP.
  • Practical experience working with large-scale training environments involving 64+ GPUs.
  • Strong understanding of large-scale pre-training data pipelines, including data cleaning, deduplication, quality filtering, tokenization, data mixing, and data quality optimization.
  • Experience designing or optimizing data pipelines for large-scale LLM training.
  • Strong ability to analyze training loss, gradients, convergence, and training stability.
  • Proven experience troubleshooting and resolving issues in large-scale distributed training environments.
  • Familiarity with long-context training and context-extension techniques, including
  • RoPE scaling, NTK-aware interpolation, and YaRN.

Preferred Qualifications

  • Experience with 70B+ parameter model pre-training.
  • Experience with Mixture-of-Experts (MoE) model pre-training.
  • Experience optimizing large-scale GPU clusters, distributed training systems, and AI training infrastructure.
  • Publications in top-tier AI/ML conferences such as NeurIPS, ICML, ICLR, ACL, or EMNLP, particularly in areas related to LLM pre-training, model architecture, or training optimization.
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