AI Research Assistant — Machine Learning & AI

About Studyfetch StudyFetch is the #1 AI-native learning platform globally, transforming how millions of students learn through personalized AI-powered education. We’re growing fast with backing from top-tier investors and a mission that’s redefining the future of education and ethical learning. About the Role We are looking for a highly motivated Master's student in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field to join our AI research team on a part-time basis. Our research focuses primarily on the intersection of artificial intelligence and education, including the development and evaluation of AI systems that can improve teaching and learning. You will work directly with researchers and engineers on projects involving machine learning, generative AI, large language models, multimodal AI, educational data, model evaluation, and AI systems. This is a hands-on research position. You will not simply be assisting with administrative research tasks—you will be expected to read papers, implement ideas, run experiments, analyze results, and contribute to the development of new research directions. Depending on your interests and experience, your work may include developing and evaluating models, building research datasets, designing benchmarks and evaluation methodologies, reproducing published research, fine-tuning open-source models, analyzing educational data, and investigating novel applications of AI in education. You will also contribute to the broader research development process, including identifying relevant research opportunities, analyzing RFPs and funding opportunities, and assisting with grant and research proposal development. What You'll Do Read and analyze recent research papers in machine learning, AI, and AI in education. Implement and reproduce methods from recent research. Design and conduct controlled experiments and ablation studies. Train, fine-tune, and evaluate machine learning models. Develop datasets and data-processing pipelines for AI research. Build and maintain evaluation and benchmarking systems. Analyze model performance and experimental results. Investigate new approaches to improving AI capabilities, reliability, and effectiveness in educational settings. Develop research prototypes in Python and modern ML frameworks. Analyze educational datasets and student interaction data to identify research opportunities and patterns. Document experiments, findings, and methodologies. Collaborate with researchers and engineers to refine research hypotheses. Contribute to technical reports, research papers, presentations, and potentially open-source projects. Research and analyze RFPs, grant opportunities, and government or foundation funding programs relevant to AI and education. Assist with the development of grant proposals, research proposals, technical narratives, and supporting materials. Help identify research questions and proposed technical approaches that align with funding opportunities. Track relevant developments in AI research, education technology, and government research priorities. Areas of Research Our research primarily focuses on the application and development of AI for education. Projects may span several areas, including: Large Language Models (LLMs) Generative AI Multimodal AI Computer vision Natural language processing AI tutoring and educational agents Personalized learning Student modeling and learning analytics Model training and fine-tuning Reinforcement learning and post-training AI evaluation and benchmarking Synthetic data generation Representation learning Retrieval-augmented generation (RAG) Educational datasets and data infrastructure AI safety, reliability, and evaluation in education You do not need experience in all of these areas. Depth in one area, strong research fundamentals, and demonstrated ability to learn quickly are more important than breadth. Required Qualifications Currently pursuing or recently completed a Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, Electrical Engineering, or a closely related field. Strong foundation in machine learning and deep learning. Strong Python programming skills. Experience with at least one modern ML framework, preferably PyTorch. Familiarity with fundamental concepts in neural networks, optimization, and statistical learning. Ability to read and understand technical research papers. Experience conducting technical or academic projects involving machine learning. Strong analytical and problem-solving skills. Strong written communication skills and the ability to clearly explain technical concepts. Ability to work independently while collaborating closely with a research team. Genuine interest in the application of AI to education. Preferred Qualifications Experience with LLMs, Transformers, multimodal models, computer vision, NLP, reinforcement learning, or generative AI. Experience fine-tuning or training machine learning models. Experience working with GPU-based ML environments. Familiarity with Hugging Face Transformers or similar ML ecosystems. Experience designing datasets, benchmarks, or model evaluations. Experience reproducing results from published research. Research experience through a Master's thesis, research lab, internship, or independent project. Experience working with educational, student, or learning data. Experience with AI evaluation, benchmarking, or experimental design. Publications, preprints, conference submissions, or other demonstrated research output. Contributions to open-source ML/AI projects. Familiarity with Linux, Git, Docker, or distributed computing. Experience researching or writing grant proposals, RFP responses, technical proposals, or research funding applications. Familiarity with federal, state, foundation, or other research funding programs. What We're Looking For We're particularly interested in people who are curious, technically rigorous, and excited by the process of discovering something that doesn't already have an obvious answer. The strongest candidates will be able to demonstrate that they have gone beyond simply completing coursework—for example, by: Building and training their own models. Reproducing a research paper. Conducting an independent ML research project. Developing a substantial Master's thesis. Creating an interesting dataset or benchmark. Investigating why a model succeeds or fails. Working with real-world educational or student data. Publishing or presenting research. Contributing to an open-source ML project. Writing or contributing to a research proposal or grant application. You do not need to have published a paper to be successful in this role. We care more about your ability to think scientifically, write good code, design meaningful experiments, communicate clearly, and learn quickly. We also value candidates who are interested in the broader research process—not just model development—including identifying research opportunities, understanding funding priorities, analyzing RFPs, and helping translate technical ideas into compelling research proposals. Position Details Position: Part-Time AI Research Assistant Focus: Machine Learning & AI for Education Hours: A pproximately 15–25 hours per week Location: [Remote / Hybrid] Schedule: Flexible schedule designed to accommodate graduate coursework and research commitments Compensation: $35–50/hour , depending on experience and qualifications This position is designed for a graduate student who wants meaningful, hands-on experience conducting applied AI research in an industry research environment, with opportunities to contribute to research publications, funded research initiatives, datasets, benchmarks, and real-world AI systems. #LI-SF1