Overview
About Qargo
Qargo is a tech scale-up with offices based in London and Ghent. We are on a mission to transform the transportation industry by making it more efficient, profitable, and sustainable. Together we are building the most user-friendly and intuitive Transport Management System (TMS) on the market that automates administrative processes and optimizes planning.
At Qargo, we are constantly looking for ways to save transport companies time and prevent planning and administrative errors using AI. We believe in an iterative approach: start with a small MVP (minimum viable product) that aids the user, then further automate based on user feedback. This means that as an AI engineer, you must learn to think like the user and understand what truly helps them save time.
The specific content of the internship can be discussed based on where your interests overlap with the AI team’s ongoing R&D tracks. Below are some potential projects to get you interested!
What you’ll do
✅ Example project: AI-powered data transformations
Modern transport companies exchange digital data with many different parties. They receive information like incoming transport orders, status updates from partner networks, and real-time tracking of resources. They also send data such as orders to subcontractors and status updates to partner networks.
To enable this exchange, our internal data schemas must be mapped to hundreds of different input/output data formats. We’ve already built an extensive editor that defines how this data is transformed.
To scale this further, we’re looking to implement generative AI to assist in defining and editing these data transformations. This will allow not just engineers and technical staff to manage these transformations, but also onboarding managers and end users.
In this case, internship will focus on building, iterating, and improving this system.
✅ Example project: invoice scanning & charge matching
Transport companies often work together to subcontract orders to one another. When a transport has been completed, the subcontractor typically sends a weekly invoice to their contractor. The contractor then needs to match the charges in that invoice to the completed orders to make an accurate profit-and-loss analysis or to charge the cost to their customer.
To streamline this process, the AI team at Qargo aims to automate this process. The system scans the invoice PDF, detects the charge lines, and matches them to the correct orders in the system.
In this case, internship will focus on iterating on this system and improving it.
What We’re Looking For
✅ You are currently pursuing a Master’s degree in Computer Science, Informatics, or related fields.
✅ You are interested in generative AI, natural language processing (NLP), and generally interesting data problems!
✅ You like building a high-accuracy AI/data pipeline, while also focusing on user interaction with the AI system and designing interfaces that minimize the impact of errors and keep the user in control.
✅ You are enthusiastic about contributing to real-world projects and making a meaningful impact.
✅ You are experienced with Python (experience with Django, Jupyter, Pandas, Matplotlib/seaborn are a plus!) and SQL.
✅ You are looking for an internship with a duration of 6 to 8 weeks (based on student availability), starting beginning of July.
What We Offer
✅ Hands-on experience with modern and varied technologies and best practices in software engineering. You’ll get to know our technical stack consisting of Python, Django, GraphQL, Kubernetes, React, and more.
✅ A chance to build a solution to a real problem, from concept to production-ready code.
✅ A supportive internship environment where you can learn and collaborate with a talented engineering team.
✅ A green office with a great atmosphere in Ghent!
✅ Experience working in a fast-paced growing startup environment.
Company:
Qargo
Qualifications:
Language requirements:
Specific requirements:
Educational level:
Level of experience (years):
Senior (5+ years of experience)
About Qargo
Qargo is a road transport management system.
