Working Student - Labelling (Robotics)

Munich

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FERNRIDE is an equal opportunity employer. We are committed to Diversity, Equity, Inclusion & Belonging because we value and celebrate everyone's differences and individuality. We strive to create an open, safe space in which you feel empowered and authentic. FERNRIDE has over 130 employees, from 35+ countries. Our culture is characterized by the company values and fundamentals:

 #wewinasoneteam #weexecuteanddeliver #weareambitiousinnovators #weareopentochange #weputcustomervaluefirst #respect #professionalism #safety.

What you will work on

At FERNRIDE, our Engineering – Autonomy team is building the intelligence that powers our autonomous and teleoperated yard trucks. A core enabler of our autonomy is high-quality labelled data across perception, prediction, and decision-making domains.

As a Working Student – Labelling (Robotics) (f/d/m), you will play a critical supporting role in developing safe and reliable autonomy by annotating multi-sensor datasets used in model training, testing, and validation. Your work will directly impact how our models interpret the environment and respond to dynamic, real-world conditions.

How you will leave your footprint

  • Label Lidar, image, and video data according to detailed internal annotation guidelines
  • Ensure consistency and precision in all labelled data used for training and evaluation
  • Flag edge cases, data ambiguities, or anomalies to improve labelling clarity and tooling
  • Collaborate with autonomy engineers to align on use cases, edge conditions, and quality expectations
  • Contribute to continuous improvements of our labelling process and documentation
  • Support validation and review of labelled datasets to uphold high-quality training standards
  • Use and potentially improve automation scripts written in Python

What you will bring to the team

  • You are currently enrolled in a Bachelor’s or Master’s program in computer science, robotics, cognitive systems, AI, or a related field
  • You are reliable, focused, and meticulous – especially when working on repetitive tasks.
  • You are fluent in English (spoken and written)
  • You are comfortable using digital tools and are quick to learn new systems
  • Prior exposure to machine learning or data annotation tools is a plus 
  • Prior experience programming in Python is a plus

What we offer @ FERNRIDE

At FERNRIDE, we believe in empowering you to thrive both personally and professionally. Our benefits are thoughtfully designed to support your growth, well-being, and aspirations while fostering a strong sense of work-life harmony. Here’s how we support you:

  • Flexible working hours.
  • All-day breakfast and unlimited drinks, fruits, and snacks
  • Lunch subsidy
  • Team, department, and company events
  • 20 days of vacation

"FERNRIDE does not require external assistance for recruitment. All hiring decisions are managed internally. We kindly request the recruitment agencies, consultancies, and other third-party services to refrain from contacting us regarding our open positions."

Who we are

FERNRIDE offers scalable automation solutions for yard trucking that increase productivity, promote sustainability, and improve worker safety. We employ a human-assisted automation approach, which allows for remote takeovers of electric trucks when necessary. This ensures seamless integration and reliable operations for logistics operators. With over a decade of research and high-profile customers, including Volkswagen, HHLA, DB Schenker, and BSH, FERNRIDE uses cutting-edge technology to address major industry challenges, such as driver shortages and the negative environmental impact of logistics operations.

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* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

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Category: Research Jobs

Tags: Computer Science Engineering Lidar Machine Learning Model training Python Research Robotics Testing

Perks/benefits: Career development Flex hours Flex vacation Startup environment Team events Unlimited paid time off

Region: Europe
Country: Germany

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