We are looking for a passionate and driven Machine Learning Engineer Intern to join our team. This is a research-heavy role, ideal for candidates who thrive on reading and implementing advanced research papers, devising innovative techniques, and working on cutting-edge AI challenges. You will play a pivotal role in building, training, fine-tuning, and deploying multi-modal models tailored for parsing complex unstructured data. Key Responsibilities Conduct in-depth research by reading academic papers and identifying state-of-the-art methods relevant to our domain. Build, train, and fine-tune multi-modal models to accurately extract and process information from unstructured data. Curate and manage custom datasets, ensuring they are tailored for training and benchmarking models. Experiment with and improve state-of-the-art machine learning and deep learning models to achieve higher accuracy and efficiency. Benchmark model performance against datasets, focusing on accuracy, efficiency, and robustness. Requirements Demonstrated experience in academic research, projects, or publications related to AI/ML, particularly in the areas of multi-modal models and transformer-based architectures. Strong programming skills in Python and familiarity with ML frameworks such as TensorFlow or PyTorch. Experience with deploying machine learning models, including containerization using Docker and other MLOps tools (e.g., MLFlow, Kubernetes, or similar). Knowledge of end-to-end deployment pipelines. Compensation: 1.5 lakh INR per month for remote role / 6k usd per month for in-office role in SF office. Location: Remote or in-office Duration: 2 months initially, extended or converted to full-time role based on performance

About Unisoled

Unsiloed converts complex documents into LLM-ready data. Our production-grade APIs ingest, parse, structure, and split documents across formats like PDF, PPT and Excel into clean Markdown and JSON that AI agents and LLMs can reliably work with from day one. It preserves document structure and hierarchy while capturing domain-specific context critical for high-stakes workflows across verticals like Finance, Legal and Healthcare.