Machine Learning Working Student

About the Company:

Tools for Humanity (TFH) designs and builds technology behind World. World is building a real human network designed to accelerate people in the age of AI. As bots and autonomous agents reshape the internet, people, institutions, and applications need a trusted way to confirm who is a real human while preserving privacy. The TFH and World tech stacks make this possible: the Orb verifies real, unique people, World ID proves it privately, and World App puts these capabilities, and more, in people’s hands. Together, they add a human layer to an AI-driven internet.

World is already running at a global scale. More than 17 million people across 160 countries have verified with World ID, and more new Orb verifications take place each week. World App is already among the most used wallets globally. Developers are integrating World ID to build safer online experiences and create spaces where real people can participate, earn, and be recognized in ways AI simply can’t replicate.

Founded in 2019, TFH has more than 400 people across hardware, software, AI, cryptography, mobile engineering, and global operations. Our teams come from OpenAI, Tesla, SpaceX, Apple, Google, Stripe, Meta, Coinbase, Palantir and MIT Media Lab. We’re backed by leading investors, including a16z, Khosla Ventures, Bain Capital Crypto, Blockchain Capital, Variant, Tiger Global, and Coinbase Ventures, as well as prominent operators and founders across fintech and AI.

TFH and World have been featured on the cover of TIME Magazine, highlighted in Fast Company’s Next 5 in Fintech, and explored in a Bloomberg deep dive. The New York Times, Bankless and TechCrunch have all recognized our collective progress in identity, cryptography, AI, and global-scale hardware deployment. Our leadership is also named to the Time AI 100. Learn more about the newest product launches from our Liftoff event.

Location: Munich (Werksviertel)

Company: Tools For Humanity GmbH

Start: Immediately

Employment: Working student / Werkstudent — 20 hrs/week (exact days/times flexible to fit your lectures and exams)

What to expect

You’ll join a small, multidisciplinary team working on real machine-learning production pipelines: dataset curation, annotation tooling, quality control, and hands-on data collection projects. This role is ideal if you’re curious about AI and computer vision, want practical experience, and take pride in precision and process. No previous ML experience required — we’ll train you.

Your tasks

• Perform image and data annotation (bounding boxes, labels, metadata corrections) using internal annotation tools and dashboards.
• Carry out quality assurance and consistency checks on labeled datasets.
• Help prepare and clean datasets for training ML models (data selection, basic preprocessing, audit logging).
• Support data-collection workflows and documentation (instructions, task checks, and participant metadata when applicable).
• Log issues, contribute to annotation guidelines, and help iterate on tooling and workflows (we use
• Streamlit dashboards, MongoDB backends and in-house labelling tools).
• Learn and apply privacy, ethics and data-handling best practices when working with sensitive biometric data.
• Optional: support small scripting tasks (Python) or join product/engineering sprint meetings.

Your profile

• Currently enrolled at a university (required for a working-student contract).
• Highly detail-oriented and consistent — you care about accuracy even in repetitive tasks.
• Fast learner and adaptable — you pick up new tools and processes quickly.
• Strong communicator: asks questions early, documents work clearly, and collaborates well in a team.
• Comfortable with basic technical tools (web apps, spreadsheets).
• Good command of English; German is an advantage but not mandatory.
• Nice to have: basic Python, familiarity with databases (MongoDB or Snowflake) and prior annotation experience.

We offer