Two software engineer candidates describe JHU APL on-site days that rotate through several groups, mixing background and behavioral questions with basic technical fundamentals like Big O and Fibonacci complexity.
$102K
Avg. Base Comp
$132K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Software engineer candidates at JHU APL describe on-site days built around meeting many people rather than facing a single technical gauntlet. One candidate flew to Baltimore, with flight, hotel and rental car covered, met five people from different groups, and also got a facility tour. Another described a full day of back-to-back interviews, roughly eight hours, in which different groups kept returning to the same questions about background, Agile experience and tech stacks.
The technical questions in these reports are fundamentals. One candidate was asked to compare the time complexity of recursive versus iterative Fibonacci. The other was asked about Big O for operations on linked lists and arrays, along with basic React and JavaScript questions. Behavioral prompts across the two reports included talking through your background, describing the most challenging problem you've solved, and naming your weaknesses.
The two experiences diverged on tone. The candidate who accepted an offer came away impressed by the people and the range of work across groups. The other found coordination uneven: interviewers seemed unclear on why the candidate was there, and a promised end-of-day debrief from HR never happened. That candidate heard weeks later that one group was interested, after already accepting another offer. If you interview here, ask who you will meet and what happens next before you leave, be ready to tell your story clearly several times, and brush up on complexity analysis.
Synthesized from 5 candidate reports by our editorial team.
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Real interview reports from people who went through the The Johns Hopkins University Applied Physics Laboratory process.
I applied online and heard back about a month later. The interview process consisted of two rounds, both conducted over Zoom. The first round was with a junior engineer and a manager, lasting around 30 minutes. They started by asking me to walk through my resume and highlight relevant experience, then moved into behavioral territory with questions like why I wanted to work at APL. The second round was a panel interview with multiple engineers from the team. Overall, the interviews were about 90% behavioral and 10% technical. One of the technical questions I encountered was reversing a singly linked list, but there was no formal coding interview or live coding session — just a discussion of the approach. The interviewers were friendly and genuinely interested in my background. They asked about how I manage my workload when overwhelmed and wanted to understand difficult problems I'd faced and how I approached them. The emphasis was really on past team experiences and how I'd handled challenges in previous internships. There was plenty of time for me to ask questions at the end, which was nice because I got a real sense of the team dynamic and what working there would be like.
Prep tip from this candidate
Focus on preparing concrete examples from past internships or projects to illustrate how you've handled difficult problems and managed workload stress. While one candidate was asked to reverse a singly linked list, the process is heavily behavioral — prepare stories that demonstrate teamwork and resilience rather than grinding LeetCode.
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Topics based on recent interview experiences.
Featured question at The Johns Hopkins University Applied Physics Laboratory
Given two sorted lists, write a function to merge them into one sorted list.
| Question | |
|---|---|
| Over-Budget Projects | |
| First Touch Attribution | |
| Find the Missing Number | |
| Employee Project Budgets | |
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| One Element Removed | |
| Level Of Rain Water In 2D Terrain | |
| P-value to a Layman | |
| Project Budget Error | |
| String Mapping | |
| Find Duplicate Numbers in a List | |
| Words in Encrypted String | |
| Target Indices | |
| Swiping App Design | |
| Slow SQL Query | |
| Nightly Job | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Loan Model | |
| Seller Type Modeling | |
| User Event Data Pipeline | |
| Subway Machine Learning Model | |
| Testing Constraints | |
| Model Deployment Pipeline | |
| Deciding Between Solutions | |
| Text Editor With OOP | |
| Three Indexes Adding Zero | |
| Fixed-Length Arrays: Deletion | |
| Client Solution Pushback | |
| Green Dot |
Synthesized from candidate reports. Individual experiences may vary.
Both software engineer reports describe meeting several groups in a single visit. One candidate met five people from different groups in Baltimore, with travel covered, and took a facility tour. Another described roughly eight hours of back-to-back interviews with different groups.
Candidates were asked to talk through their background, experience with Agile and various tech stacks, the most challenging problem they had solved, and their weaknesses. In one report the questions overlapped between interviewers, so have concise versions of your main stories ready.
One candidate was asked about the time complexity of recursive versus iterative Fibonacci. Another was asked about Big O for operations on linked lists and arrays, plus basic React and JavaScript questions. Neither report describes a heavier coding exercise.
One candidate accepted an offer after the visit. The other said HR never delivered the promised end-of-day debrief and was slow to respond on scheduling, and heard weeks later that one group was interested, after accepting an offer elsewhere. Confirm next steps before you leave.
