AI Research Assistant — Machine Learning & AI

StudyFetch•Beverly Hills, CA

1d•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.