In 2026 expect recommender and modelling design (a marketplace recommender with cold-start users and sparsity, aligning offline and online evaluation), ML rigour questions (detecting label leakage before it inflates offline metrics), metric reasoning tied to business cost (choosing a precision-recall operating threshold where false positives annoy customers), plus a coding round and ML-breadth questions. Interviewers weight practical judgement, the offline-online gap, and clear communication over exotic architectures, and every round is scored against principles like Dive Deep and Are Right, A Lot. Prepare ML fundamentals, applied design, coding, and STAR stories.
About Amazon
Largest e-commerce + AWS cloud + Alexa + Prime Video + Ring.
- Products
- Amazon.com / Amazon India · AWS · Alexa / Echo · Prime Video · Kindle
- Competitors
- Microsoft Azure / GCP (cloud) · Walmart / Flipkart (commerce) · Netflix (video)
Amazon recruitment process
01
Recruiter screen and online assessment
02
Technical phone screen on ML and coding
03
Onsite loop: ML design, ML breadth, coding, and Leadership Principles
04
What to expect in each round
01
Round 1 (60 min)
ML fundamentals and coding screen.
02
Round 2 (60 min)
ML system and modelling design round (recommenders, evaluation).
03
Round 3 (60 min)
ML breadth and rigour round with metric reasoning.
04
Round 4 (45-60 min)
Leadership-Principles behavioural round.
Technical questions Amazon asked
Sourced from 2+ candidate post-mortems. Hit Practice to answer any one with AI voice feedback.
- 01
Hard
How do you set the bar for promoting an SE3 to Staff? Walk me through the last time you said 'not yet', what was missing?
- 02
Hard
Given a list of orders with timestamps, find the K most frequently ordered items in a time range. Optimise it, then state your complexity.
- 03
Medium
Given a grid of warehouse cells with obstacles, find the shortest path a robot can take from pickup to drop. Which traversal, and why?
- 04
Hard
Design an in-memory structure that returns the current most-recently-used product for a session in O(1). Walk me through the data structures.
- 05
Hard
Given a large fact table of orders and a dimension table of customers, write SQL to compute month-over-month retention cohorts. Optimise for a columnar warehouse like Redshift.
- 06
Medium
Design an ETL pipeline that loads daily transactional data from operational databases into a warehouse, handling schema drift and idempotent reloads. What do you use on AWS?
- 07
Medium
How do you design a data-quality framework that catches broken pipelines and bad data before it reaches business dashboards? What checks and alerts do you add?
- 08
Hard
Design a recommendation model for a marketplace with millions of items and cold-start users. How do you handle sparsity and evaluate offline versus online?
- 09
Medium
How would you detect and correct for label leakage in a fraud-detection model before it silently inflates offline metrics?
- 10
Medium
Explain precision, recall, and how you'd choose an operating threshold for a model that flags suspicious transactions where false positives annoy customers.