Data Validation & AI Training Intern

About the Role

We are looking for a Data Validation & AI Training Intern to support the quality of structured data and AI-assisted outputs across our internal products and projects.

This is a hands-on role involving research, verification, data review, annotation, quality checks, and documentation. You will work with structured records, identify inaccuracies or inconsistencies, apply defined quality guidelines, and provide clear feedback that helps improve data quality and AI-assisted workflows.

Responsibilities

• Review structured data and AI-assisted outputs for accuracy, completeness, consistency, and duplication
• Verify information using reliable and authoritative sources
• Identify incorrect, incomplete, inconsistent, or unsupported information
• Correct and standardize data according to defined quality guidelines
• Flag uncertain or conflicting information for further review
• Perform data labelling and annotation tasks with consistency
• Document recurring errors, unusual cases, and quality issues
• Maintain clear records of validation decisions and corrections
• Follow established data-quality and review procedures
• Suggest practical improvements to validation and review workflows
• Collaborate with data, product, and engineering teams when required

Requirements

• Strong attention to detail
• Good research and analytical skills
• Ability to work carefully with structured information
• Sound judgement when identifying inaccurate or inconsistent data
• Comfort working with spreadsheets, web-based tools, and structured datasets
• Ability to follow detailed guidelines consistently
• Clear written communication
• Ability to document decisions and findings
• Basic understanding of artificial intelligence and machine learning concepts
• Willingness to learn data annotation, validation, and AI quality-assurance practices
• Ability to work responsibly in a remote environment

Preferred Background

Previous experience is helpful but not mandatory in areas such as:

Students and recent graduates with strong analytical ability and attention to detail are encouraged to apply.

What You Will Learn

During the internship, you will gain practical experience in:

• Structured-data validation
• Research and source verification
• Data-quality practices
• AI output evaluation
• Data annotation and labelling
• Identifying recurring quality issues
• Handling ambiguous and conflicting information
• Documenting findings and escalation decisions
• Working with production-oriented product and engineering teams
• Improving operational data-review processes

Who This Role Is Best Suited For

This role is suitable for someone who enjoys investigating information, identifying inconsistencies, working methodically with data, and understanding how high-quality data contributes to reliable AI and software systems.

We value careful thinking and accuracy more than prior experience with any specific tool or technology.

Selection process

1. Application review

   We review how clearly the applicant thinks and communicates, not only academic marks or résumé keywords.

2. Practical exercise

   Shortlisted applicants may receive a small product, research, writing, design, or engineering exercise relevant to the opportunity.

3. Discussion

   The discussion focuses on the submitted work, learning approach, availability, and expectations.

4. Final decision

   Selected applicants receive written details about the opportunity, responsibilities, expected commitment, and engagement terms.