Amazon ML Engineer interviews typically include a tough HackerRank-style OA, coding rounds, an ML-focused system design or ranking-system round, and leadership-principles behavioral questions; senior candidates may find team matching on level and location harder than the interview itself.
$183K
Avg. Base Comp
$254K
Avg. Total Comp
4-5 rounds
Typical Rounds
Not reported
Process Length
Amazon's ML Engineer loop, based on recent candidate reports, centers on two things: a HackerRank-style online assessment that can be genuinely hard and an ML-focused system design round. One candidate described the OA as the main filter: they could not finish the second of two LeetCode-style problems and could not find a solution online afterward. Another candidate cleared the online assessment without much trouble, so expect the difficulty to vary rather than assume a fixed bar.
Once past the screen, the loop typically includes two coding interviews (medium-difficulty problems on trees and monotonic stacks have both been reported), an ML system design or ML breadth round, and behavioral questions tied to Amazon's leadership principles. The ML rounds stand out. One candidate was asked to design a ranking system or ML algorithm and reason about time complexity, including getting operations down to O(log n). Another was asked how they'd handle a production ML problem the team was actually facing rather than a textbook scenario. Behavioral questions were sometimes framed as pointed "gotchas" rather than open-ended prompts.
A separate thread worth knowing about: for senior and staff-level ML engineers, the harder challenge can be team matching rather than the interview itself. One staff-level candidate saw open roles repeatedly scoped to L5 and tied to Seattle or East Coast relocation. If level and location matter to you, raise them with the team early. This guide draws on a small set of candidate reports, so treat any single detail as one data point rather than a fixed pattern.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Amazon process.
I have been trying to find the right ML Engineer team at Amazon during my remaining team-match window, but the biggest issue has been level and location rather than a conventional interview loop. I work as a staff-level ML engineer/research engineer, and each time I speak with an Amazon team, the role is ultimately scoped down to L5 and requires relocation to either Seattle or the East Coast. I am specifically looking for a Bay Area role, ideally connected to edge AI, inference, or closely related MLE work.
The team-matching process has been difficult and has felt more like searching for a compatible opening than demonstrating technical ability in a standard interview. I was open to accepting the downgrade and lower compensation if it meant joining a relevant Bay Area team, but I have repeatedly declined because the available teams required a move. My advice is to clarify level expectations and location requirements with the team as early as possible, particularly if you are targeting a specialized area such as edge AI or inference.
Prep tip from this candidate
Ask prospective teams early whether the role is Bay Area-based and whether they can support your target level; the key constraint here was team match, not a disclosed technical interview format.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Amazon
Write a query to return whether each user's subscription date range overlaps with any other completed subscription
| Question | |
|---|---|
| Merge Sorted Lists | |
| Compute Deviation | |
| Permutation Palindrome | |
| Bagging vs Boosting | |
| P-value to a Layman | |
| Prime to N | |
| Get Top N Frequent Words | |
| Compute Variance | |
| Nearest Common Ancestor | |
| Recurring Character | |
| Random Forest Explanation | |
| Bank Fraud Model | |
| Jars and Coins | |
| Type-ahead Search | |
| Encoding Categorical Features | |
| Same Algorithm Different Success | |
| Find the First Non-Repeating Character in a String | |
| Missing Housing Data | |
| Weekly Aggregation | |
| Valid Anagram | |
| Flatten JSON | |
| RMS Error | |
| Booking Regression | |
| Xgboost vs Random Forest | |
| Hurdles In Data Projects | |
| Dice Worth Rolling | |
| Biased Random Number Generator | |
| Production Model Monitoring | |
| Lasso vs Ridge |
Synthesized from candidate reports. Individual experiences may vary.
After applying, one candidate received a HackerRank-style online assessment with two coding questions. They found the second problem especially difficult, could not finish it, and called it the main filter in the process. Another candidate described their online assessment as straightforward. Difficulty may vary by role and level, so practicing timed LeetCode-style problems, including harder ones you may not be able to brute-force quickly, is a reasonable way to prepare.
One candidate reports that after the online assessment, Amazon scheduled an HR call followed by a one-hour coding interview with a team member before moving into the main loop. Only one candidate mentioned this extra technical touchpoint ahead of the final rounds, so it may not appear in every ML Engineer process.
Both candidates who described the final loop report two coding interviews, generally rated easy to medium. One candidate was asked a tree problem and a separate monotonic-stack question, both medium difficulty. The monotonic-stack question was framed abstractly enough to add extra friction. Expect standard data-structure and algorithm questions, but be ready to reason through less familiar framings.
In these reports, the system design portion of the loop leans ML-specific rather than generic. One candidate was asked to design ML algorithms or a ranking system and justify time-complexity choices, such as getting key operations down to O(log n). Another faced a system design question tied to a problem their team was working on, plus a separate ML breadth round covering foundational ML concepts and how they'd approach a production problem. Both candidates flagged this ML depth as a key area to prepare for.
Behavioral questions tied to Amazon's leadership principles appear in these reports, sometimes as a dedicated round and sometimes interspersed between technical interviews. One candidate found the questions felt more like gotchas than open-ended prompts, asking about situations that didn't map cleanly onto their actual experience. Preparing specific, honest stories in advance rather than improvising in the moment may help you avoid getting caught off guard here.
One staff-level candidate reports that the biggest obstacle wasn't a technical interview at all but team matching. Available ML Engineer openings were repeatedly scoped down to L5 and required relocating to Seattle or the East Coast rather than staying in the Bay Area. If you're targeting a specific level, location, or specialization such as edge AI or inference, this candidate suggests clarifying it with the team as early as possible.
