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AI Researcher at TensorOps. . Location: Remote. 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. . .