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IBM interview notes, page 2 of 4

Large Enterprises

Most of these IBM write-ups come from Software Engineers (32), then Data Scientists (6) and Product Managers (1). The most reported rounds are the technical phone screen (25), the online assessment (10), the hiring manager screen (1) and the behavioral / leadership round (1).

39 interview notes · updated Oct 2026

IBM·Software Engineer·Technical Phone Screen

May 2026
IBM software engineer coding round with one algorithmic problem about merging time intervals. Pretty standard stuff but there were some subtle edge cases worth thinking through.
  • Given n intervals where each interval [start, end] is inclusive on both ends, compute the total amount of time during which at least one process is running.

“First thing I fumbled was the inclusive endpoint part.”

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IBM·Software Engineer·Technical Phone Screen

May 2026
IBM DevSecOps interview that leaned pretty hard into Linux internals. One main question but it had a lot of surface area and I felt like I was being tested on breadth more than depth.
  • Given a running process's PID, what Linux commands would you use to inspect it, and what does each one tell you?

“I started with ps and top because those are the obvious ones, then remembered /proc and that's where things got interesting.”

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IBM·Data Scientist·Online Assessment (OA)

May 2026
Took the IBM Data Science OA blind and it was a rough ride. The SQL part was fine if you knew the right function, but the coding question was genuinely brutal and I basically gave up after passing 2 out of 15 test cases.
  • SQL question requiring knowledge of a specific built-in SQL function.
  • A coding/algorithmic problem that required a combinatorics-based insight rather than any standard brute-force approach.

“Not bad if you happen to know the function they're looking for.”

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IBM·Data Scientist·Technical Phone Screen

May 2026
IBM data scientist internship interview for a marketing analytics team. One main question that was really more of a guided case study, asking you to walk through two different modeling approaches on the same project. Felt more like a structured conversation than a typical interview.
  • Walk me through a project where you built both a statistical model (like logistic regression or a GLM) and a machine learning model (like a tree-based method or boosting). Cover the business problem, how you defined the target variable, what features you used and why, how you handled stakeholder expectations around interpretability versus performance, and what you'd do differently looking back.

“This is a lot to hold in your head at once.”

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IBM·Data Scientist·Technical Phone Screen

May 2026
IBM data scientist interview that was basically one big case question about free trial conversion modeling. Felt more like a stats exam than a typical DS interview, which I wasn't fully expecting.
  • How would you precisely define 'conversion probability' for a cohort of free trial users, including what time window you'd use and how you'd define the cohort itself?
  • How do you handle users whose trial hasn't ended yet when estimating conversion probability for a cohort?
  • How would you account for users who convert after their trial period ends, so-called delayed conversions?
  • If you segment conversion rates by channel or country and notice differences, how do you handle potential confounding, for example if the channel mix has shifted over time?
  • How would you quantify and communicate uncertainty around your conversion probability estimate?
  • What follow-up analyses or guardrail metrics would you suggest to make sure your conversion estimate is actually useful for decision-making?

“I fumbled the time window part initially.”

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IBM·Software Engineer·Technical Phone Screen

May 2026
Technical screen for a DevSecOps role at IBM, pretty deep on Kubernetes tooling. The whole session basically revolved around ArgoCD and GitOps patterns, which I was not expecting to be the entire focus.
  • How does ArgoCD implement the GitOps model, and why is Git treated as the source of truth for deployments?
  • Walk me through the ArgoCD Application resource. What are the key fields and what does ArgoCD actually do with it?
  • What is the difference between manual sync and auto-sync in ArgoCD, and when would you choose one over the other?
  • How does ArgoCD reconcile the live cluster state against the desired state in Git? What happens when they drift?
  • How would you handle multi-environment promotion with ArgoCD, for example promoting a release from staging to production?
  • How do you roll back a deployment using ArgoCD, and how does that interact with the GitOps principle of Git being the source of truth?

“I gave a reasonable answer about Git holding the desired state and ArgoCD polling for changes, but I fumbled a bit explaining WHY that model is better than imperative kubectl commands.”

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IBM·Software Engineer·Technical Phone Screen

Jun 2026
IBM software engineer interview that went deep on open-source background. One big multi-part question covering everything from specific repos and contributions to how you handled community dynamics and backward compatibility. Felt more like a portfolio review than a typical coding screen.
  • Walk me through your open-source contributions in detail: which projects, what you actually did, what had the most impact, how you worked with maintainers and the community, and what you took away from the experience.

“This question is basically six questions duct-taped together and they want you to cover all of it.”

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IBM·Software Engineer·Technical Phone Screen

Jun 2026
Did a coding round at IBM for a software engineer role. Pretty standard algorithmic stuff, one question on sliding windows that I thought I had but second-guessed myself halfway through.
  • Given an integer array and an integer k, find the minimum length of a contiguous subarray that contains exactly k distinct numbers. Return -1 if no valid subarray exists.

“I knew sliding window was the right move but fumbled the part where you shrink from the left while keeping the distinct count at exactly k.”

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IBM·Software Engineer·Technical Phone Screen

Jun 2026
IBM software engineer interview with a coding question focused on implementing 1-D convolution from scratch. Pretty straightforward once you get past the terminology, but there are a few gotchas worth knowing about.
  • Implement a valid 1-D convolution function using cross-correlation semantics. Given an input array, a kernel array, and a scalar bias, compute output values only where the kernel fully overlaps the input (no padding, stride of 1), adding the bias to each output element. Handle edge cases like an empty kernel or a kernel longer than the input, and analyze the time and space complexity of your solution.

“The word 'convolution' threw me off at first because I kept thinking about the flipped-kernel version from math class.”

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IBM·Software Engineer·Technical Phone Screen

Jun 2026
IBM software engineer interview with a single coding problem. Nothing too wild but the overflow gotcha is easy to miss if you're not thinking carefully.
  • Count how many numbers of the form 3^x * 5^y fall within a given range.

“Pretty approachable once you realize you just enumerate all valid combinations.”

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