Tell me about the experiment project or POC you worked on
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DoorDash
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Lockheed Martin
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What is this question about
Interviewers use this question to see how you operate when the path is unclear and the outcome is uncertain. A strong answer shows how you framed the unknown, chose a practical way to test it, learned from evidence, and turned that learning into a decision or next step. For more senior candidates, it also reveals whether you used experiments to reduce risk for a team or organization rather than just exploring something interesting.
“Can you walk me through a proof of concept or exploratory project you worked on?”
“Tell me about a time you tested an idea before committing to a full build.”
“What's an example of a focused experiment you ran to reduce technical uncertainty?”
“Describe a situation where you built something small to answer a bigger engineering question.”
Key Insights
- You should not treat the experiment as a miniature feature launch. The point is usually how you reduced uncertainty efficiently, not how much code you wrote.
- Be explicit about the decision the experiment was meant to inform. Many candidates describe what they built but never explain what question they were trying to answer or what changed because of it.
- If the experiment disproved your original idea, that can be a strong answer. Interviewers often like candidates who can stop, pivot, or narrow scope based on evidence instead of forcing a preferred solution through.
What interviewers probe atlevel
Top Priority
At junior level, show that the experiment had a real purpose and that you understood what unknown you were helping clarify.
Good examples
🟢Our team wasn't sure whether a third-party API could handle the response time we needed, so I tested that specific question before we committed to integrating it.
🟢We had two ways to process files and didn't know which would fit our memory limits, so I helped design a small experiment to compare them.
Bad examples
🔴I wanted to try a new framework because it seemed modern, so I built a quick version in it and showed it to my mentor.
🔴We already knew we were moving forward, but I made a proof of concept anyway so we could say we had explored options.
Weak answers center on curiosity or activity; strong answers center on a concrete unknown that affected a real decision.
A strong junior answer shows you looked at what the experiment taught you, even if the result was not what you hoped for.
Good examples
🟢The test showed memory usage was much higher than we expected, so we decided not to use that approach yet.
🟢One part of the experiment passed and another failed, so I summarized both and recommended a narrower use instead of a full adoption.
Bad examples
🔴The prototype worked on my machine, so I concluded the approach was good enough to move forward.
🔴I preferred the new tool, and the proof of concept mostly confirmed my initial impression.
Weak answers treat the experiment as validation for a prechosen answer; strong answers let the evidence shape the conclusion.
Valuable
Do not end your story at 'I built it'; show what happened next and what you learned from the experiment.
Good examples
🟢I documented the result, shared the tradeoffs with my team, and we used it to choose the safer implementation path.
🟢After the test, I wrote down what assumptions had been wrong so the next task started with better information.
Bad examples
🔴I demoed the proof of concept and everyone liked it, so that was basically the end of the project.
🔴I finished the experiment and handed it to my lead, who took it from there.
Weak answers stop at completion; strong answers show the experiment changed understanding or informed the next step.
Example answers atlevel
Great answers
In my last internship, our team needed to decide whether a vendor API was fast enough for a user-facing feature, and my manager asked me to help test that assumption. I built a very small program that called the API with sample requests and measured response times under a few realistic conditions instead of trying to build the whole feature. The results showed it was fine for normal traffic but too slow for one of our larger request sizes. I wrote up the findings, including what I had and hadn't tested, and shared them with my mentor and the team. We ended up using the API only for the smaller requests and keeping the existing path for the larger ones. What I liked about that project was learning that a good experiment is really about answering one important question quickly, not building something polished.
At the small ed‑tech nonprofit where I work, teachers were regularly telling us that updating lesson content felt confusing, so I was asked to see if a simpler editor would actually help. I built a lightweight, in-browser prototype (just HTML/CSS and a bit of JavaScript—no backend) that exposed only the core editing tasks and ran short, 20-minute usability sessions with five teachers. I timed how long they took to complete common actions and collected verbal feedback; teachers finished tasks about twice as fast and repeatedly asked for clearer save feedback. I summarized the findings with annotated screenshots and a short list of must-have features and trade-offs. The product lead used that to scope a small, prioritized implementation, and I enjoyed how quickly a low-cost experiment let real users steer our work before we committed engineering time.
Poor answers
I worked on a proof of concept for a new front-end framework that I thought would be better for our app. I rebuilt one of our screens in it and added a lot of the real behavior so people could see how it would look in production. The demo went well and the team agreed it was a cool direction. After that, my lead kept the idea in mind for future work. It was a good project because I got to show initiative and learn a new tool.
Question Timeline
See when this question was last asked and where, including any notes left by other candidates.
Late May, 2026
Lockheed Martin
Staff
Late April, 2026
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Senior
Late August, 2025
DoorDash
Mid-level
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