Myntra Interview Questions 2026: Software Engineer and Data Roles

Myntra's engineering is organised around product domain teams. The Discovery team builds the search, recommendation, and personalisation systems that surface products to users. Tech: Java Spring Boot for the search microservice (built on Elasticsearch), Python and PyTorch for ML recommendation models, and Apache Kafka for real-time behavioural event streaming. The Visual Intelligence team works on visual search (find similar products from a photo), outfit completion (recommend complementary items), and fit prediction (size recommendation from body measurements and past purchases). Tech: Python, PyTorch, convolutional neural networks (CNNs), AWS S3 for image storage. The Fulfillment team manages Myntra's supply chain: from vendor warehousing through quality check, packaging, and last-mile delivery via M-Express (Myntra's 24-hour delivery service in metro cities). Tech: Java for OMS (Order Management System), Python for route optimisation, and real-time tracking via WebSocket APIs. The Platform team owns the web frontend (React with TypeScript), React Native mobile app, GraphQL API gateway, and backend infrastructure. Myntra's web performance is closely tracked (Core Web Vitals), and the frontend team regularly ships A/B tests across the 50M+ user funnel.

Myntra's hiring follows Flipkart's structure (since they share HR and recruiting). Round 1 (Online Assessment): 90 minutes on HackerRank. Two to three coding problems at LeetCode medium level in Java or Python. Common problem types: arrays and strings, binary search, sliding window, and graph traversal (BFS/DFS). Round 2 (Technical Interview 1 — DSA): 60 minutes. Two medium-to-hard algorithmic problems with discussion of time and space complexity. Common topics: dynamic programming (e.g., longest increasing subsequence for trending size predictions), heap-based problems (e.g., top-K popular products in a time window), and sliding window for real-time user behaviour analysis. Round 3 (Technical Interview 2 — System Design): 60-75 minutes. 'Design the Myntra product recommendations system', 'Design a real-time search autocomplete for 50 million users', or 'Design the M-Express delivery slot booking system'. Candidates are evaluated on schema design, data flow, caching strategy (Redis for session and product caches), and scalability trade-offs. Round 4 (Bar Raiser): Flipkart and Myntra conduct a bar-raiser round for mid-to-senior roles, where an interviewer from outside the hiring team evaluates the candidate against a high bar. This round often revisits DSA or does a short system design followed by behavioural questions.

For data science and ML engineering roles at Myntra, understanding fashion-specific ML problems significantly differentiates candidates. Recommendation: Myntra uses a hybrid collaborative-content filtering approach. Collaborative filtering (users similar to you also bought X), content-based filtering (this product has similar colour and style to what you bought), and context-aware ranking (time of day, device, session behaviour). Knowing the cold-start problem (how to recommend to new users or promote new products with no interaction history) and how Myntra uses editorial curation alongside ML to solve it is useful. Visual Search: Myntra's visual search lets users upload a photo and find similar products. The ML pipeline: image preprocessing, CNN feature extraction (embedding into a 512-dimensional vector), approximate nearest neighbour search (FAISS or ScaNN), and re-ranking by availability, price, and brand tier. Size Prediction: Myntra's FitNote predicts the right size for a user based on their past purchase and return history, their body measurements (if provided), and the brand's size chart. This requires handling sparse data (users buy rarely) and cross-brand normalisation. For A/B testing and product analytics roles, Myntra runs a large-scale experimentation platform where hundreds of A/B tests run simultaneously across the product surface.

Myntra aligns to Flipkart's compensation structure (Flipkart RSUs form a significant part of the package). 2026 salary benchmarks: Junior SWE (0-2 years): Rs 20-35 LPA total comp. Mid-level SWE (2-5 years): Rs 35-60 LPA. Senior SWE (5+ years): Rs 55-90 LPA. Staff Engineer / Principal: Rs 90-140 LPA. Data Scientist (3-5 years): Rs 30-60 LPA. Senior Data Scientist: Rs 55-90 LPA. Flipkart RSUs vest over 4 years (1-year cliff, monthly after) and provide liquidity through Flipkart's secondary market programs since Flipkart remains private. The Bengaluru headquarters has a strong product-engineering culture with frequent hackathons, tech talks, and internal mobility across Myntra, Flipkart, and PhonePe for senior talent.