AI Engineering Intern
International Machine Learning Olympiad
United StatesFresher1h agovia LinkedIn
This job at a glance
- Who can apply
- New grads and up to 1 year
- Must know
- Machine learningData structures & algorithmsStakeholder managementPythonscikit-learnPandas
- Work
- On-site in United States · internship
- Pay
- Not stated by the employer.
- Skill demand
- Python72%Machine learning53%Stakeholder management29%Data structures & algorithms14%of 99 similar entry-level jobs posted in the US this week
- Listed
- Nov 4, 2026 · still open
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About the Role
The AI Engineering Intern is a full-time remote role focused on supporting the development, experimentation, and deployment of machine learning and AI solutions used within IMLO programs and competitions.
Key Responsibilities
- Assisting in building and testing ML models.
- Cleaning and preparing datasets.
- Implementing algorithms in code.
- Contributing to internal tools and pipelines.
- Documenting technical work.
- Helping maintain reproducible experiments.
- Collaborating on research-driven projects.
- Participating in code reviews.
- Preparing technical summaries or demos for student participants and stakeholders.
Requirements & Tech Stack
- Foundational skills in programming and software development.
- Proficiency in Python.
- Familiarity with libraries commonly used in machine learning (e.g., NumPy, pandas, scikit-learn).
- Core machine learning and AI skills, including understanding of supervised and unsupervised learning, basic neural networks, and experience implementing standard algorithms.
- Data handling and analysis capabilities, such as data cleaning, feature engineering, exploratory data analysis, and working with structured and unstructured datasets.
- Problem-solving and research-oriented skills, including the ability to read technical papers, experiment with new ideas, and evaluate model performance using appropriate metrics.
- Collaboration and communication skills, including clear written documentation, ability to work effectively in remote teams, and openness to feedback and mentoring.
- Familiarity with deep learning frameworks (such as PyTorch or TensorFlow) are beneficial.
- Experience with version control systems (e.g., Git) are beneficial.
- Interest in AI competitions, open-source projects, or educational initiatives.
- Commitment to ethical and responsible AI development.
Qualifications
- Currently pursuing or recently completed a degree in Computer Science, Data Science, Engineering, Mathematics, or a related field, or equivalent practical experience.