全职 LLM Pre-training Engineer 工作, 薪水 up to SGD 10,000, UMICHAT (SINGAPORE) PTE. LTD. 公司招聘中 - Ricebowl

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 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.
• 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.

Pay: $5,000.00 - $10,000.00 per month

Benefits:

• Additional leave
• Food provided
• Health insurance
• Professional development

Work Location: In person