AI Researcher

Responsibilities

• Fine-tuning pre-trained LLMs on small to medium datasets (500-20k examples)
• Implementing parameter-efficient fine-tuning (e.g., LoRA-style methods)
• Optimising training for cost and performance
• Running experiments on GPU cloud infrastructure
• Evaluating model performance and tradeoffs (specialisation vs generalisation)
• Deploying fine-tuned models for inference

Requirements

• Strong Python skills
• Experience with deep learning frameworks: PyTorch (preferred) or TensorFlow
• Experience with Hugging Face Transformers or similar ecosystems
• Hands-on experience training or fine-tuning transformer models on GPUs (local or cloud-based)
• Previous experience using cloud platforms for model training or deployment (e.g., AWS, GCP, Azure, RunPod or similar GPU providers)
• Experience working with or fine-tuning open-weight LLM families (Gemma-3, Qwen-3.5, Llama 4, GPT-OSS, Mistral...)
• Hands-on experience with LoRA
• Understanding of:
• Fine-tuning vs pretraining
• Overfitting and generalization
• Model evaluation
• Strong business awareness: ability to understand the context of the fine-tuning task and translate domain requirements into clear modeling objectives
• What you bring
• MSc or PhD student in Computer Science, Machine Learning, AI, or related field
• Alternatively, 6 months of hands-on experience training and fine-tuning deep learning models
• Has worked on LLMs in research or industry
• Has fine-tuned at least one transformer model
• Comfortable working independently
• Interested in applied AI and real-world constraints (cost, latency, memory)
• What You'll Gain
• Real-world experience fine-tuning large models (30B-100B parameter class)
• Exposure to production constraints and deployment
• Opportunity to co-author technical writeups if applicable
• Strong applied portfolio project

Benefits

Additional Information

Location: Remote Duration: 2-4 months (project-based) Type: Contract / Research Collaboration (Paid) About the Project We are looking for a Master's or PhD student to work on fine-tuning large language models (LLMs) for domain-specific tasks. The goal is to take an existing pretrained model (e.g., Meta AI's LLaMA-class models or similar) and specialize it for a narrow, high-value use case using efficient fine-tuning techniques. This is a hands-on applied project designed for someone who wants real-world experience deploying and optimising LLM systems. Help drive the next wave of applied AI by demonstrating how fine-tuned LLMs can unlock advanced, real-world use cases beyond general-purpose foundation models. Organizations that require domain-specific accuracy, self-hosted deployments, customisable workflows, or performance beyond out-of-the-box capabilities increasingly rely on fine-tuned models to meet those needs. Through this project, you will contribute to building specialised AI systems that deliver improved accuracy, efficiency, and control compared to out-of-the-box models. You will also help bridge the gap between academic knowledge and real-world application by applying fine-tuning techniques to solve concrete business problems.

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Company Intel

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