Mechanical Engineering Internship

present of AI; Paris; Hybrid; EUR 13,000 - 20,000; Part time

Job summary

HyLight is seeking a final-year master's student to work between CAD design of new airship parts and hands-on workshop fabrication and testing. A strong candidate has completed real mechanical projects through internships, student teams or personal builds, and is comfortable prototyping and tracing why parts fail.

Reliability thinking, design for manufacturing, and basic data analysis with Python complete the profile, linking mechanical engineering to AI-driven inspection workflows.

Qualifications

• Final year master's student able to split CAD design and hands-on workshop work.
• Real mechanical projects through internships, student teams, or personal builds.
• Comfortable testing, prototyping, and tracing why parts fail.
• Reliability thinking, design for manufacturing, and basic data analysis.

Responsibilities

• Design new airship parts and test in the workshop.
• Run life tests and fix field failures.
• Prototype and iterate based on test results and failure analysis.
• Analyze test data (Python) to support root-cause investigation and design decisions.

Skills

CAD design

Mechanical testing

Fatigue testing

Root cause analysis

Python

Education

Master's student in Mechanical Engineering

Job description

The role asks for a final year master's student who can move between CAD design of new airship parts and hands on work in the workshop. A strong candidate has done real mechanical projects, whether through internships, student teams or personal builds, and is comfortable testing, building prototypes and tracing why parts fail. Reliability thinking, design for manufacturing and basic data analysis round out the profile.

How it fits the AI era

The airship itself is the physical product, so most of this work stays hands on: designing parts, running life tests, fixing field failures. AI enters through the data side, where flights return LiDAR, imagery and methane readings that models turn into defect reports, and that feedback loop shapes which mechanical fixes matter most. The human judgment is in deciding what to redesign and proving it holds up in the field.

How it builds AI experience

An intern here would see how test and failure data feed the automated inspection pipeline, and could build skills in analyzing test results with Python to support root cause work. Learning how reliability data connects to the AI that flags defects on the grid gives a practical view of where mechanical engineering meets machine learning.

Worth showing

• Prototyping and workshop builds
• Mechanical testing and fatigue
• Root cause analysis methods
• Python for test data

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About HyLight

Autonomous, hydrogen-powered airships for zero-emission aerial inspection of critical energy and transport grids.