Machine Learning Working Student

Role overview

This working-student opportunity is based in Munich and sits within a small, cross-functional team focused on real-world machine learning operations. The work centers on dataset preparation, annotation support, quality control, and data-collection activities. It is suited to someone who wants practical exposure to AI and computer vision, values accuracy, and is comfortable learning by doing. Prior machine-learning experience is not required, as training will be provided.

About the company

Tools for Humanity (TFH) develops the technology that powers World, a global network built to help confirm that a person is real while preserving privacy in an internet increasingly shaped by AI systems. Its technology stack includes an Orb for verifying unique people, World ID for private proof of human identity, and World App for bringing these tools into users’ hands.

World is already operating at worldwide scale, with more than 17 million verified people across 160 countries and new Orb verifications happening every week. World App is among the most widely used wallets globally, and developers are using World ID to create safer online experiences where real users can participate and be recognized.

Founded in 2019, TFH now has over 400 employees working across hardware, software, AI, cryptography, mobile engineering, and global operations. The team includes people with backgrounds from leading companies and research groups, and the company is supported by well-known investors and operators in fintech and AI.

TFH and World have received coverage and recognition from major publications and industry outlets for their work in identity, cryptography, AI, and large-scale hardware deployment.

Location and setup

The role is located in Munich, Werksviertel, and is an in-office position. The employer listed for the role is Tools For Humanity GmbH.

The start date is immediate.

The schedule is 20 hours per week, with the exact days and times arranged flexibly to suit lectures, tutorials, and exam periods.

What you will do

• Label images and data using internal tools, including bounding boxes, tags, and metadata fixes.
• Review labeled datasets for consistency and quality.
• Assist with preparing and cleaning training data, including selection, basic preprocessing, and audit logging.
• Support data collection processes, including instructions, task validation, and participant metadata where relevant.
• Record issues, help improve annotation instructions, and contribute to better tooling and workflows.
• Work with internal systems such as Streamlit dashboards, MongoDB-backed tools, and in-house labeling software.
• Follow privacy, ethics, and data-handling standards when dealing with sensitive biometric information.
• Optionally contribute small Python scripting tasks or take part in product and engineering sprint meetings.

Candidate profile

• You must currently be enrolled at a university, since the position is structured as a working-student contract.
• You should be very precise, steady, and attentive to detail, especially when work is repetitive.
• You should be comfortable learning new tools and processes quickly.
• Clear communication is important: you should ask questions early, document your work well, and collaborate effectively.
• You should be comfortable using basic digital tools such as web applications and spreadsheets.
• English proficiency is required.
• German is helpful but not mandatory.
• Helpful extras include basic Python knowledge, familiarity with MongoDB or Snowflake, and prior annotation experience.

What is offered

• Hands-on experience on live ML systems and datasets with visible impact on production workflows.
• Room to suggest improvements to processes and annotation rules, with guidance from experienced team members.
• A flexible 20-hour weekly schedule that works around academic commitments.
• A modern office in Munich’s Werksviertel with strong transport connections, refreshments, and meals provided three times per day.
• Pay that is positioned above the usual market level for student roles.
• Training in data privacy, ethical handling of biometric data, and the company’s internal tools, along with ongoing mentorship.
• Possible long-term opportunities after graduation, including the chance to extend the role or complete a thesis with the team.

Application details

Applicants are asked to send a CV and a short cover letter of one to two paragraphs. The message should include the earliest possible start date, weekly availability in hours, and the current degree program and year of study.

Contact person: Constantin Ingelheim, August-Everding-Straße 25, 81671 Munich, Germany.

The company states that it participates in the E-Verify Program and follows equal opportunity and affirmative action practices. It also says that reasonable accommodations are available for candidates with disabilities during the hiring process.

Additional information

This role is specifically meant for currently enrolled students under a working-student arrangement. The company emphasizes precision, repeatable process work, and the handling of sensitive biometric data with care and responsibility.

The employer also notes that the role offers exposure to machine-learning production workflows rather than purely theoretical work, and that training will be provided for candidates without prior ML experience.