AI & Machine Learning Intern (Remote, Virtual Internship)

INTERNECTUAL; United States; Remote; USD 21,000 - 34,000; Part time

Benefits offered by this job

Flexible schedule

Work from home

Job summary

INTERNECTUAL is offering a fully remote, project-based AI internship. You will join the Applied AI team and work on real client briefs to turn messy data into predictive models. You’ll own models from raw data to a working API and explain results to non-technical clients.

The role focuses on forecasting, risk flags, ranking and classification, with emphasis on reproducibility, evaluation metrics, and client-ready documentation.

Qualifications

• Comfortable writing basic Python and exploring data.
• Curious about how data turns into decisions.
• Open to students, recent graduates, or career switchers.

Responsibilities

• Clean, explore and document real-world datasets before modelling.
• Build baseline models and improve them with evidence.
• Engineer reproducible pipelines in Python ecosystems.
• Serve models via an API (e.g., FastAPI) and validate inputs.
• Document model limitations, bias and usage in simple terms.
• Deliver results with clear README guidance for clients.

Skills

Python basics

Data curiosity

Team collaboration

Education

Student or recent graduate

Tools

Pandas

scikit-learn

PyTorch

FastAPI

Jupyter

Git & GitHub

Docker

Job description

About internectual.in

internectual.in is a remote, project-based virtual internship programme. Interns work in weekly sprints on realistic client briefs, submit their work in an in-browser workspace and receive a score and detailed review on every task.

About the role (INT-AIML-01, Applied AI team)

Turn messy business data into models that make predictions people rely on. You will join the Applied AI team and work on prediction problems that come from client briefs: forecasting demand, flagging risk, ranking and classifying. You own each model from raw data to a working API, and you explain your results to a non-technical client the way a real ML engineer does.

What you will do

• Clean, explore and document real-world datasets before any modelling
• Build baseline models first, then improve them with evidence, not guesswork
• Choose the right metric for the business problem and report it honestly
• Engineer features and build reproducible scikit-learn / PyTorch pipelines
• Serve trained models through a FastAPI endpoint with input validation
• Write model cards that explain limitations, bias and intended use
• Ship every deliverable to GitHub with a README a client could follow

What you will learn

• Take a vague business question and frame it as an ML problem
• Train, tune and compare classical and deep-learning models
• Detect overfitting, data leakage and class imbalance before they bite
• Deploy a model behind an API and monitor it for drift
• Present results with charts and plain-language trade-offs

Examples of what you will build

• A delivery-delay predictor served as an API
• A churn model with an explainability report
• An image or text classifier using transfer learning

Tools: Python, pandas, scikit-learn, PyTorch, FastAPI, Jupyter, Git & GitHub, Docker

Who can apply

• Comfortable writing basic Python (loops, functions, lists, dictionaries)
• Curious about how data turns into decisions
• Students in any year, recent graduates and career switchers are welcome

Programme details

• Fully remote, flexible hours (15-20 hours per week)
• Duration: 3, 4 or 6 months, one sprint every week
• Weekly performance report, Employee ID card and appointment letter on joining
• Completion certificate and experience letter (QR-verifiable) on successful completion; letter of recommendation for top performers

Benefits

• Flexible schedule
• Work from home

Work Location: Remote