Generative AI Intern: Fine-Tune LLMs & Global Impact

Razer Inc.; Singapore; On-site; SGD 13,000 - 22,000; Part time

Job summary

Razer Inc. invites you to join our AI Software team as a Generative AI Intern.

You will help develop and optimize large language models for intelligent AI applications, working on dataset preparation, model fine-tuning, evaluation, and training optimization. You will gain hands-on experience with distributed training, PyTorch, Hugging Face, and advanced optimization techniques, while collaborating with a global team across continents.

Qualifications

• Pursuing Bachelor's or Master's in CS, AI, Data Science or related field.
• Proficient in Python and familiar with PyTorch or TensorFlow.
• Knowledge of ML, DL and Transformer models.
• Familiarity with Hugging Face, distributed computing or Linux is advantageous.
• Analytical, detail-oriented and motivated to learn.
• Available for a minimum of 16–20 week internship.

Responsibilities

• Prepare and validate datasets for model fine-tuning and evaluation.
• Fine-tune language models using LoRA, supervised fine-tuning and preference optimization.
• Support distributed training and experimentation using PyTorch, Hugging Face, Ray or DeepSpeed.
• Benchmark model quality, latency, memory usage and training performance.
• Explore optimization techniques such as quantization and model distillation.
• Analyze experimental results and document findings.

Skills

Python

PyTorch

TensorFlow

Machine learning

Deep learning

Transformer models

Hugging Face

Linux

Analytical thinking

Detail-oriented

Motivated to learn

Education

Bachelor's/Master's in CS/AI/Data Science

Job description

Razer Inc. invites you to join our AI Software team as a Generative AI Intern.

You will help develop and optimize large language models for intelligent AI applications, working on dataset preparation, model fine-tuning, evaluation, and training optimization. You will gain hands-on experience with distributed training, PyTorch, Hugging Face, and advanced optimization techniques, while collaborating with a global team across continents.