AI Intern
Posted Sep 11, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about a machine learning model you built end to end. Where did the data come from, and how did you decide it was good enough?
What they are checking: Whether they own the whole loop from data to evaluation rather than just calling a training function.
Tell me about a time a model or experiment didn't work. What did you learn and what did you try next?
What they are checking: Whether they debug methodically and stay steady when results disappoint.
Which tools and frameworks are you most comfortable with, and which one did you have to learn fast for a project?
What they are checking: Whether their claimed skills are hands-on and whether they can pick up new tools quickly.
What would you do
These products keep firefighters and soldiers safe. How does that change the way you think about a model that's wrong some of the time?
What they are checking: Whether they weigh the cost of false positives and false negatives in a safety setting.
You're paired with mechanical and electrical engineers who don't know much about AI. How would you explain what your model can and can't do?
What they are checking: Whether they can set honest expectations with non-specialists who will build around their work.
You've got ten weeks and a vague project. How do you scope it so you have something real to show at the end?
What they are checking: Whether they can break down open-ended work and deliver a finished result inside an internship timeline.
About the work
Say you're training a computer vision model on imagery from a work truck's cameras. What problems do you expect in that data that you wouldn't see in a clean public dataset?
What they are checking: Whether they understand real sensor data is messy, with glare, dirt, vibration and rare events.
A model runs fine on a GPU server but has to run on an edge device inside a vehicle. What would you change to make it fit?
What they are checking: Whether they know practical tools like quantization, pruning and smaller architectures for limited hardware.
How would you build a data pipeline that takes raw sensor logs and turns them into something you can train on?
What they are checking: Whether they think about labeling, cleaning, versioning and repeatability instead of one-off scripts.
Your model scores 95 percent accuracy, but the event you care about only happens in 2 percent of the data. How do you judge whether it's actually good?
What they are checking: Whether they know accuracy hides failure on rare classes and reach for precision, recall or confusion matrices.
What does the phrase software-defined vehicle mean to you, and where could AI fit into it?
What they are checking: Whether they have real curiosity about the vehicle domain rather than only generic AI interest.
Practice this interview out loud.
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Practice this out loudThe full job description
As published by Oshkosh Corporation.