Artificial Intelligence Intern

Artificial Intelligence Intern – Deep Learning & Foundation Models
Location: United States | Remote / Hybrid
Engagement: Internship
Compensation: Fixed
Work Authorization: Must be authorized to work in the United States. Visa sponsorship is not available.
About the Opportunity
We are an early-stage, US-based deep technology company developing proprietary AI foundation models and advanced predictive systems at the intersection of artificial intelligence, scientific computing, and complex data intelligence.
We are seeking a highly motivated, technically strong, and research-oriented Artificial Intelligence Intern to contribute to the design, development, training, and evaluation of advanced machine learning architectures.
This is a hands-on opportunity to work on original AI models, complex scientific datasets, and challenging research problems alongside an interdisciplinary engineering and research team.
Required Qualifications
Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, or a related technical field.
Strong programming proficiency in Python, with practical experience using PyTorch or TensorFlow.
Solid understanding of machine learning algorithms, neural networks, and deep learning fundamentals.
Familiarity with CNNs, GNNs, Transformers, or other modern neural architectures.
Strong mathematical foundations in linear algebra, probability, statistics, and optimization.
Experience with NumPy, Pandas, scikit-learn, and Git.
Ability to independently implement, debug, and evaluate machine learning models.
Strong analytical thinking, research curiosity, and problem-solving skills.
Preferred Qualifications
Strong knowledge of diffusion models, generative AI, or foundation model architectures.
Familiarity with RAG, GraphRAG, knowledge graphs, LLMs, and vector databases.
Exposure to geometric deep learning, equivariant neural networks, or multimodal AI.
Experience with PyTorch Geometric, Hugging Face, LangChain, LlamaIndex, or related frameworks.
Knowledge of self-supervised learning, contrastive learning, and representation learning.
Experience with GPU computing, cloud infrastructure, or distributed training.
Previous AI/ML research, publications, open-source contributions, or advanced technical projects.
Interest in scientific machine learning, computational modeling, and interdisciplinary AI research.
What We're Looking For
We are looking for intellectually curious and technically ambitious individuals who enjoy solving challenging problems and experimenting with advanced AI architectures.
The ideal candidate demonstrates:
Strong fundamentals in machine learning and deep learning.
Ability to understand and implement technical research papers.
Experience building AI models beyond standard API integrations.
A structured approach to experimentation, debugging, and model evaluation.
Independence, ownership, and the ability to thrive in a fast-paced research-driven environment.
This internship focuses on original AI model development, research engineering, and foundational machine learning—not primarily prompt engineering, chatbot development, or third-party API integration.
Engagement & Compensation
Location: United States — Remote / Hybrid, depending on project requirements.
Engagement: Internship with opportunities to contribute to ongoing AI research and engineering initiatives.
Compensation: Open to discussion based on experience, technical capabilities, and internship scope.
Work Authorization: Applicants must be legally authorized to work in the United States. We do not offer employment-based visa sponsorship now or in the future.
How to Apply
Interested candidates should submit:
Resume or CV
GitHub profile or technical portfolio
Relevant AI/ML projects, research publications, or model implementations
A brief description of their technical interests and experience
We welcome candidates who are passionate about advancing AI research and contributing to the development of proprietary, next-generation intelligence systems.

Work arrangement
Yes