HackTheRounds Interview Experiences

Amazon New Grad SDE Interview Experience (2026) - Coding, GenAI & Bar Raiser

A four round Amazon new grad loop with binary search, prefix sums, tree coding, process scheduling, practical GenAI judgment, and Leadership Principles.

By HackTheRounds Team · 2026-07-31

Background

This four-round Amazon new-grad report combined coding and Leadership Principles throughout the loop. It also included questions about responsible use of generative AI, which is becoming part of some 2026 engineering interviews. The public Chinese-language source did not disclose a final hiring result or exact interview day, so the outcome remains unknown and the article's July 31 publication date is used transparently.

Round 1: Rotated Search and Circular Subarray

Problem 1: Find a target in a rotated sorted array in logarithmic time.

I began each binary-search iteration by deciding which side of the midpoint was sorted. Once that invariant was known, the target range determined which half could be discarded. I also discussed the ambiguous case created by duplicate boundary values; shrinking equal left and right boundaries avoids getting stuck, though it weakens the worst case.

Problem 2: Find the maximum subarray sum when the subarray may wrap from the end of an array to the beginning.

The answer is the better of two cases: the normal maximum subarray, or the total sum minus the minimum subarray. The important exception is an all-negative input. In that case, subtracting the minimum subarray represents choosing an empty complement, so the ordinary maximum element must win.

After coding, I tested single-element arrays, a pivot at either boundary, a missing target, all-negative values, and the non-wrapping optimum.

Round 2: Prefix Sums, Project Work, and Leadership Principles

Problem: Given an array and k , find the maximum-sum contiguous subarray whose endpoint values differ by exactly k .

I used prefix sums and scanned the right endpoint from left to right. For a right value x , a valid left endpoint must contain either x - k or x + k . A map stored the smallest eligible prefix value observed for each array value, allowing the best sum ending at the current position to be computed in constant expected time.

The subtle point was what the map stored. Presence alone was insufficient; retaining the minimum prefix for each endpoint value maximized the eventual difference. I stated the index convention carefully so the prefix subtracted corresponded to the position just before the left endpoint.

The rest of the round moved into a project deep dive and questions around Ownership and Dive Deep. I started with the problem and my responsibility rather than listing technologies. For each story, I included the signal that changed my mind, the action I took, and what mechanism remained after the immediate fix.

Round 3: Responsible GenAI, Tree Coding, and Process Scheduling

The interviewer asked how I use GenAI during development and, more importantly, how I verify its output. I described using it to enumerate edge cases, explain unfamiliar modules, and propose alternatives while keeping review responsibility with the engineer. Generated changes still need tests, static analysis, code review, and cautious rollout. Sensitive data and high-impact operations require additional controls.

The coding question was Lowest Common Ancestor in a binary tree. I clarified whether parent pointers existed and whether both nodes were guaranteed to appear. With no parent pointer, a postorder traversal returns a target when found; the first node receiving a match from both sides is the common ancestor. For a very deep tree, an explicit stack or parent map avoids recursion-depth risk.

The design prompt was a process-scheduling service. I asked about priorities, retry rules, and latency targets before drawing components. My design used a priority queue for ready work, leases when workers claimed jobs, and stable idempotency keys for side effects. If a worker died, an expired lease returned the job to the queue without treating every retry as a new task.

Round 4: Bar Raiser

The final round centered on disagreement and decision making under incomplete information. Follow-ups asked what data existed, what I opposed specifically, which alternative costs I considered, who was affected, and what happened when the result was weaker than expected.

I kept “I” and “we” distinct. Team context mattered, but the interviewer needed to understand my judgment and responsibility. I also avoided turning every story into an effortless success. A credible account included the risk I missed, how quickly I surfaced it, and what I changed afterward.

Outcome

The source did not publish a final decision. This post therefore does not infer an offer from completing the four rounds.

Tips

  1. Practice binary-search invariants and the all-negative exception in circular Kadane problems.
  2. In prefix-sum problems, write down exactly which prefix index a map value represents.
  3. Prepare Leadership Principles stories that survive multiple layers of evidence and tradeoff questions.
  4. Explain GenAI use through verification and risk boundaries, not a list of tools.
  5. In worker systems, discuss leases, retry policy, idempotency, and poison jobs together.
  6. Never convert a completed loop into an assumed offer; keep unknown outcomes labeled unknown.
  7. Rehearse transitions between coding and behavioral discussion because the loop can switch evaluation modes without a separate break.