Free tool · no sign-up · 4 interview rounds
Generate AI-powered Meesho interview questions for all 4 rounds. Process: Online Coding → Technical × 2 → System Design → HR.
From our curated bank of 10 Meesho-specific questions. The generator produces fresh AI-tailored questions each run.
What is Meesho and what makes their engineering challenges unique?
Tip: Meesho: social commerce platform connecting resellers and suppliers in tier-2/3 India. Unique challenges: extremely low-bandwidth users (2G/3G), vernacular language support (12+ Indian languages), 10M+ SKUs from unorganised suppliers (noisy catalog data), and cash-on-delivery (COD) logistics optimisation.
How would you design Meesho's product catalog system for 50M+ SKUs from unstructured supplier data?
Tip: Ingestion: supplier uploads images + text → ML pipeline (product categorisation, attribute extraction, duplicate detection). Storage: Elasticsearch for search, S3 for images, Postgres for structured metadata. De-duplication: product matching model using embeddings (same product from multiple suppliers). Quality scoring to rank catalog completeness.
Write code to find the maximum profit from buying and selling a stock with at most K transactions.
Tip: DP: dp[k][i] = max profit using at most k transactions up to day i. Transition: dp[k][i] = max(dp[k][i-1], max over j < i of: price[i] - price[j] + dp[k-1][j]). Optimise: track max(dp[k-1][j] - price[j]) as a running variable. O(kn) time, O(kn) or O(n) space with rolling array.
How does Meesho handle COD (Cash on Delivery) logistics at scale?
Tip: COD is 60–70% of Meesho's orders — unique challenges: cash collection at delivery, COD return fraud (order placed never intended to collect), remittance delays (3PL holds cash before transferring). Engineering: COD risk scoring model, automated remittance reconciliation, fraud signal (repeat non-delivery addresses).
Explain how you would implement multilingual search for Meesho's 12+ language catalog.
Tip: Approach: (1) Transliteration: Hinglish → Hindi (user types "kurti" → also searches "कुर्ती"). (2) Translation layer: query → English canonical form → Elasticsearch. (3) Language-specific analyzers in Elasticsearch (devanagari tokeniser). (4) Pre-translate popular queries. Meesho has a dedicated NLP team for this.
Practise questions for each stage to maximise your preparation.
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