Amazon ML Engineer candidates report a demanding HackerRank assessment followed by coding, ML-focused system design, and behavioral evaluation. Team matching may also hinge on level and location.
$185K
Avg. Base Comp
$280K
Avg. Total Comp
5 rounds
Typical Rounds
2 weeks
Process Length
Amazon ML Engineer candidates should prepare for an early coding filter and a later loop that combines software fundamentals with ML-oriented design. One candidate reported a HackerRank-style online assessment with two LeetCode questions; the second was notably difficult and was not completed. Problem solving may therefore matter before a candidate reaches the full interview loop.
That candidate then described an HR conversation, a one-hour coding interview with a team member, and a five-round loop: two LeetCode-style coding interviews, two ML-focused system design interviews, and one behavioral interview. The coding questions were characterized as easy to medium, while the design discussions required designing ML algorithms or a ranking system and reasoning about time complexity, including O(log n) operations. Practice explaining tradeoffs aloud: what the system optimizes, which operation is costly, and why a proposed data structure or design changes that cost.
A separate candidate's experience centered on team matching rather than a conventional technical loop. They repeatedly found roles scoped at L5 and tied to Seattle or the East Coast while seeking Bay Area work. Ask about level, location, and team scope early if those constraints matter to you. Evidence is limited to two candidate accounts, so formats may vary by team.
Synthesized from 2 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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Synthesized from candidate reports. Individual experiences may vary.
One candidate reported receiving a HackerRank-style assignment after applying, with two LeetCode questions. They found the second especially difficult and could not finish it, so timed coding practice may be useful for the initial screen.
The same candidate described an HR call followed by a one-hour coding interview with a team member. The account does not provide question-by-question detail for this interview, but it places a live coding conversation before the later loop.
One reported loop had two LeetCode-style coding interviews, two ML-focused system design interviews, and one behavioral interview. Candidates should expect the design portion to probe a ranking or ML-algorithm design and time-complexity reasoning, based on that account.
A separate candidate described team matching as the key stage, with available roles repeatedly scoped at L5 and requiring relocation to Seattle or the East Coast. This may vary by opening, but clarifying level and location early can prevent mismatch.
