Key Skills

Job Description

Governments, investors and companies make big decisions about AI every week, often based on hype, vendor pitches or guesswork. RankmyAI exists to replace that with independent data. We're now building RankmyAI 2.0, the market intelligence platform for the AI economy, and we're looking for an intern to build it with us. Location: Amsterdam (hybrid) Duration: minimum 4 months Hours: 32-40 hours/week Compensation: up to €520/month, based on study level and experience Start date: flexible, in consultation Working language: English or Dutch Eligibility: EU work permit required Application deadline: 27 October 2026 About RankmyAI RankmyAI is the independent research platform for the AI tool ecosystem. We are an initiative of the Centre for Marketing Innovation and the Centre of Expertise Applied Artificial Intelligence at Amsterdam University of Applied Sciences (HvA). We rank and track 63,000+ AI tools and publish monthly insights on how the AI economy is evolving. Governments, international organisations and AI hubs use our data for market monitors and reports, and our analysis made the front page of the Financieele Dagblad. We have the data. Now we want to use it to show what AI companies actually do, how they compare, and where the market is going. The next phase is to build out the research engine: better taxonomies, smarter data, and signals that nobody else publishes. The role You join the founding team and share responsibility for the data behind RankmyAI 2.0. Your work covers three areas. Classification. Making sure every company lands in the right place in our taxonomy, consistently, as the database grows. ⁠New data. Adding new features to the platform to compare AI companies. You pick up the high-impact ones, define how they are measured, prototype the extraction, validate quality, and bring them into the platform. Each feature adds a new dimension for comparing AI tools and for industry analysis. ⁠Insights. Finding what the data says that nobody has published yet, and writing it up so anyone can follow. In four months, you will have ⁠Measurably improved how tens of thousands of AI companies are classified ⁠Launched at least one new data feature on the live platform ⁠Published at least one analysis based on your work About you Currently pursuing an MSc or final-year BSc in Computer Science, AI, Data Science, Computational Linguistics, or a related field ⁠You're a good Python developer. You take an idea from experiment to production, and keep improving it after it's live. ⁠You've worked with machine learning, generative AI or agents, and you know when each one fits. ⁠You test your models properly: is the output right, is it reliable, is it biased, and is it worth it? ⁠You know your way around APIs, Postgres and Docker. ⁠You can explain your work to people who don't code. What you get ⁠Direct ownership of research that goes into a live product with an active audience Work across the full lifecycle, from taxonomy design through pipeline, publication, and user feedback Mentorship from the founding team ⁠Flexible hybrid working from our Amsterdam base ⁠A spot in a research initiative at exactly the moment it is gaining momentum How to apply Send us: Your CV A short note (max one page) on a classification, taxonomy, or data-quality problem you have worked on. What made it hard, and what you would do differently Optional: a link to code, a notebook, a blog post, or anything else that shows how you think Apply via LinkedIn or email [email protected]. Questions? DM David Kakanis on LinkedIn.

Core Responsibilities

Improve the classification and taxonomy of AI companies, develop and validate new data features, and integrate them into the live platform. Analyze the data to uncover new market insights and publish findings for a broad audience.

Requirements

Applicants must be pursuing an MSc or be in the final year of a BSc in Computer Science, AI, Data Science, Computational Linguistics, or a related field. They should have strong Python skills, experience with machine learning, generative AI, or agents, sound model evaluation practices, familiarity with APIs, PostgreSQL, and Docker, and the ability to explain technical work clearly.

Benefits

• Mentorship
• Flexible Hybrid Working
• Direct Ownership of Research
• Full Research Lifecycle Experience