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. What You’ll Work On 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 Experience 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 What We Offer 100% Remote Work: Work from anywhere with flexibility and autonomy Dynamic, High-Impact Projects: Work on cutting-edge ML and GenAI solutions across diverse industries International Clients: Collaborate with global organizations and solve real-world challenges at scale Urban Sports Club Membership: Supporting your physical and mental wellbeing Monthly Bolt Credits: For rides Company Events & Offsites: Regular team gatherings to connect, collaborate, and celebrate