Sprinklr Software Engineer interview typically runs 4 rounds: online assessment, technical DSA rounds, resume deep-dive, and HR. It usually takes a few weeks and is notably technical, with hard questions and detailed resume scrutiny.
$139K
Avg. Base Comp
$181K
Avg. Total Comp
4-5
Typical Rounds
3-5 weeks
Process Length
Our candidates report that Sprinklr is less interested in polished surface answers than in whether you can defend the details of your work and your code. A recurring theme is resume verification at the implementation level: interviewers opened GitHub links, asked why certain React choices were made, and pushed into framework structure, design patterns, and even Selenium or QA workflow specifics. That means the bar is not just “have you built something,” but “can you explain exactly how and why you built it this way?”
The technical signal is equally consistent: Sprinklr leans hard into problems that require real algorithmic flexibility, especially hard DP, tree, sliding window, and reframing-style questions. Multiple candidates described rounds with no easy questions, and one accepted candidate noted that simply talking through stuck points and candidate reasoning helped. We’ve also seen a strong systems layer in the process: Kubernetes, Linux, OSI/TCP fundamentals, AWS architecture, and tradeoffs like SQS vs Kafka came up repeatedly. In other words, they seem to value engineers who can move comfortably from code to infrastructure without hand-waving.
What makes or breaks candidates here is often not one brilliant answer, but whether their story holds up under pressure. The strongest experiences show interviewers probing for depth across whatever you claim on your resume, then checking whether your design choices are grounded in practical experience. Our read is that Sprinklr rewards engineers who are genuinely hands-on and can connect algorithmic rigor with production reality.
Synthetized from 3 candidates reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Sprinklr process.
The hardest part for me was that the process never really stayed at one level — it started with a pretty standard coding screen and then kept shifting between DSA and resume-based grilling. My first round was an online assessment with one medium and two hard LeetCode-style questions, so it was already more intense than a typical warm-up round. After that, I had a DSA interview where they gave me two hard questions and pushed for clean, working code that passed test cases. In another technical round, they came back to the resume and asked me to walk through what I had actually built, including details from my GitHub and even why I chose certain things in a React project. That part felt very hands-on and specific, not just high-level behavioral talk.
What surprised me most was how much they cared about the exact implementation details in my CV and repo. They opened the GitHub links I had listed and asked follow-up questions directly from the code, so it helped to know every project line by line. The DSA questions were a mix of standard and tricky: I saw a simple binary search problem, a tree problem, a sliding window question, and a difficult DP-style question. There was also a round that sounded more framework-oriented, especially around my current QA process, framework structure, design patterns, and Selenium-related questions, which made it feel like they were checking both depth and practical experience. I didn’t make it through, but the process was consistent in one way: they wanted strong coding ability and a real understanding of whatever you claimed on your resume.
Prep tip from this candidate
Be ready to defend every project and GitHub link on your resume, especially implementation choices in React or other code you’ve listed. For coding prep, focus on hard LeetCode-style DSA, including binary search, trees, sliding window, and DP, and practice writing fully working code under test-case pressure.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Sprinklr
Write a function that tests whether a string of brackets is balanced.
| Question | |
|---|---|
| Cyclic Detection | |
| Production Rollout Challenges | |
| Azure Kubernetes Infrastructure | |
| 2nd Highest Salary | |
| Top Three Salaries | |
| Merge Sorted Lists | |
| Empty Neighborhoods | |
| Subscription Overlap | |
| Rolling Bank Transactions | |
| Prime to N | |
| Random SQL Sample | |
| Raining in Seattle | |
| Top 3 Users | |
| Comments Histogram | |
| Weighted Keys | |
| Customer Orders | |
| String Shift | |
| Find the Missing Number | |
| Upsell Transactions | |
| Largest Salary by Department | |
| Employee Salaries | |
| Scrambled Tickets | |
| Bagging vs Boosting | |
| Closest SAT Scores | |
| P-value to a Layman | |
| Job Recommendation | |
| Hurdles In Data Projects | |
| Monthly Customer Report | |
| Delivery Estimate Model |
Synthesized from candidate reports. Individual experiences may vary.
Candidates start with an online coding assessment that is typically LeetCode-style and fairly difficult. Reported assessments included one medium and two hard questions, often covering topics like binary search, trees, sliding window, and dynamic programming.
The next stage usually consists of one or more live technical interviews focused on data structures and algorithms. Interviewers expect clean, working code and may push on hard problems such as DP formulations, maximal rectangle variations, LIS-style problems, and other medium-to-hard coding questions.
One technical round often shifts from pure coding to a detailed review of the candidate's resume, GitHub, and past projects. Interviewers may open linked repositories and ask very specific follow-ups about implementation choices, React projects, QA frameworks, design patterns, or Selenium-related work.
Some candidates have a hiring manager or senior technical round focused on system design and infrastructure. Topics reported include AWS architecture for frontend/backend/database deployment, Kubernetes, Linux, OSI/TCP fundamentals, SQS vs Kafka, cost management, and discussion of production incidents.
The final stage is typically an HR conversation covering motivation, expectations, and basic role fit. In some cases this round is straightforward and brief, while in others it serves as a final check before the manager-side decision.