AI Researcher

<p><strong>Location:</strong> Remote</p> <p><strong>Duration:</strong> 2–4 months (project-based)</p> <p><strong>Type:</strong> Contract / Research Collaboration (Paid)</p> <p>&nbsp;</p> <p><span style="font-size: 14pt;"><strong>About the Project</strong></span></p> <p>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.</p> <p>This is a hands-on applied project designed for someone who wants real-world experience deploying and optimising LLM systems.</p> <p>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.</p> <p>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.</p> <p>&nbsp;</p> <h3><strong>What You’ll Work On</strong></h3> <ul> <li>Fine-tuning pre-trained LLMs on small to medium datasets (500–20k examples)</li> <li>Implementing parameter-efficient fine-tuning (e.g., LoRA-style methods)</li> <li>Optimising training for cost and performance</li> <li>Running experiments on GPU cloud infrastructure</li> <li>Evaluating model performance and tradeoffs (specialisation vs generalisation)</li> <li>Deploying fine-tuned models for inference</li> </ul> <p>&nbsp;</p> <h3><strong>Experience</strong></h3> <ul> <li>Strong Python skills</li> <li>Experience with deep learning frameworks: PyTorch (preferred) or TensorFlow</li> <li>Experience with Hugging Face Transformers or similar ecosystems</li> <li>Hands-on experience training or fine-tuning transformer models on GPUs (local or cloud-based)</li> <li>Previous experience using cloud platforms for model training or deployment (e.g., AWS, GCP, Azure, RunPod or similar GPU providers)</li> <li>Experience working with or fine-tuning open-weight LLM families (Gemma-3, Qwen-3.5, Llama 4, GPT-OSS, Mistral...)</li> <li>Hands-on experience with LoRA</li> </ul> <p>&nbsp;</p> <h3><strong>Understanding of:</strong></h3> <ul> <li>Fine-tuning vs pretraining</li> <li>Overfitting and generalization</li> <li>Model evaluation</li> <li>Strong business awareness: ability to understand the context of the fine-tuning task and translate domain requirements into clear modeling objectives</li> </ul> <p>&nbsp;</p> <h3><strong>What you bring</strong></h3> <ul> <li>MSc or PhD student in Computer Science, Machine Learning, AI, or related field</li> <li>Alternatively, 6 months of hands-on experience training and fine-tuning deep learning models</li> <li>Has worked on LLMs in research or industry</li> <li>Has fine-tuned at least one transformer model</li> <li>Comfortable working independently</li> <li>Interested in applied AI and real-world constraints (cost, latency, memory)</li> </ul> <p>&nbsp;</p> <p><strong>What You’ll Gain</strong></p> <ul> <li>Real-world experience fine-tuning large models (30B–100B parameter class)</li> <li>Exposure to production constraints and deployment</li> <li>Opportunity to co-author technical writeups if applicable</li> <li>Strong applied portfolio project</li> </ul> <h3><strong>What We Offer</strong></h3> <ul> <li><strong>100% Remote Work</strong>: Work from anywhere with flexibility and autonomy&nbsp;</li> <li><strong>Dynamic, High-Impact Projects</strong>: Work on cutting-edge ML and GenAI solutions across diverse industries</li> <li><strong>International Clients</strong>: Collaborate with global organizations and solve real-world challenges at scale</li> <li><strong>Urban Sports Club Membership</strong>: Supporting your physical and mental wellbeing</li> <li><strong>Monthly Bolt Credits</strong>: For rides</li> <li><strong>Company Events &amp; Offsites</strong>: Regular team gatherings to connect, collaborate, and celebrate</li> </ul> <p>&nbsp;</p>