Mercury Insurance Software Engineer interview typically runs 1 round: technical screen. It usually takes about 1 round and is notably hands-on and more algorithmic than expected.
$118K
Avg. Base Comp
$187K
Avg. Total Comp
3-4
Typical Rounds
1-2 weeks
Process Length
We've seen Mercury Insurance lean hard into real-world execution over polished theory. In the candidate experience we reviewed, the conversation started with day-to-day responsibilities and quickly moved into hands-on work: finding a live element with Selenium, talking through endpoint test coverage, and explaining how you’d approach quality in a practical setting. That tells us the team wants engineers who can reason from the product outward, not just recite testing concepts.
A recurring theme is that the interview can feel more algorithmic than the role description suggests. One candidate was surprised by a LeetCode-style string problem layered into an otherwise applied screen, and the pressure point wasn’t just solving it — it was doing so while navigating a fairly direct interviewer style. We also noticed a subtle signal in the Google discussion: the team seemed less interested in forbidding tools outright than in seeing whether the candidate could stay resourceful and still think independently. For us, that points to a process that values adaptability, but expects you to defend your reasoning clearly when the problem gets less scripted.
The non-obvious make-or-break here is balance. Candidates who only prepare for automation tooling may get caught off guard by the coding component, while candidates who over-index on algorithms may miss the emphasis on test design grounded in actual systems behavior. Mercury appears to reward engineers who can move comfortably between browser-level debugging, API thinking, and concise problem solving without losing sight of how software is shipped and validated in practice.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Mercury Insurance process.
The first round was a technical interview with a Principal-level SDET, and it felt more like a practical screen than a theory-heavy one. They started by asking about my past job experience and what my day-to-day work looked like, then moved into real-world scenarios. One part had me sharing my screen and using Selenium to find an element on a real website with XPath, which was very hands-on. They also asked what kinds of tests I would write for a given endpoint, so they were clearly checking whether I could think through test coverage in a realistic way.
The part that stood out most was that the interview was harder than I expected for the role. I got a question that felt straight out of LeetCode: given a string, find the duplicate characters and count how many times each one appears, then print the result, like a5b3c2d4 for aaaaabbbccdddd. I asked if I could use Google since there are often built-in language methods for this kind of thing, and at first they said no. I pushed back a bit and asked whether, if I were already on the team, I’d really be expected not to look things up when I didn’t know something. They eventually said I could use Google, just not search for the solution. Overall, it was a pretty direct interview and definitely more algorithmic than the actual job seemed to be. I didn’t get an offer, so my main takeaway is to be ready for both practical Selenium/endpoint questions and at least one coding problem under pressure.
Prep tip from this candidate
Be ready to do a live Selenium XPath exercise on a real site and explain what tests you’d write for an endpoint. Also practice simple string-counting problems under interview pressure, since they asked one that was basically a duplicate-character frequency task.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Mercury Insurance
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Employee Salaries | |
| Bagging vs Boosting | |
| Random SQL Sample | |
| P-value to a Layman | |
| Hurdles In Data Projects | |
| Rectangle Overlap | |
| Size of Joins | |
| Delivery Estimate Model | |
| Always Excited Users | |
| Total Spent on Products | |
| Three Zebras | |
| Target Indices | |
| Success Measurement | |
| Duplicate Rows | |
| Type I and II Errors | |
| RAG Strict Source Control | |
| Transformer Encoder Layer | |
| Digitizing Student Test Scores | |
| Common Prefix | |
| Count Transactions | |
| Swap Variables | |
| Integer String Addition | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Data Pipelines and Aggregation | |
| The Longest Journey | |
| Moving Window | |
| Triangle as Binary Array | |
| Drink Production Allocation | |
| Green Dot |
Synthesized from candidate reports. Individual experiences may vary.
The experience suggests an initial screening to confirm background and fit before the technical round. Candidates should be prepared to summarize their past work, current responsibilities, and the kinds of engineering tasks they handle day to day.
This first technical round was highly practical and conducted by a Principal-level SDET. The interviewer asked about prior job experience, then moved into real-world testing scenarios, including what tests to write for a given endpoint and how to think about coverage in a realistic way.
A live screen-sharing portion focused on browser automation skills. The candidate was asked to use Selenium on a real website to find an element with XPath, showing that the interview emphasized applied tooling and comfort working through a task in real time.
The interview also included an algorithmic coding question that felt similar to a LeetCode-style prompt. The task was to count duplicate characters in a string and print the compressed result, so candidates should expect at least one pressure-tested programming exercise.