ShareChat Interview Questions 2026: SWE and ML Roles Guide

ShareChat's engineering is centred on content understanding, personalised recommendation, and large-scale content distribution. The backend is primarily Python for ML services and data pipelines, with Go for high-performance content serving and feed generation. React Native handles both the ShareChat and Moj mobile apps. The ML platform team uses PyTorch for model development and TensorFlow Serving for model inference in production. Content recommendation at ShareChat is built on two-tower neural networks for retrieval, followed by re-ranking models that incorporate real-time user signals (watch time, shares, comments). The content moderation infrastructure handles millions of posts per day across 15 languages, using a combination of automated classifiers (fine-tuned multilingual BERT models) and human review queues. The language diversity is the unique challenge: most off-the-shelf ML models are English-first, and adapting them for code-switched Hindi-English, Bhojpuri, or Tamil content requires significant custom engineering.

Round 1 (Online Coding Assessment): 60-90 minutes. Two to three algorithmic problems at LeetCode medium difficulty. Python is the most commonly used language. Graph algorithms, dynamic programming, and hash map optimisations appear frequently. Round 2 (Technical Interview 1): Data structures, algorithms, and system design for ML-adjacent candidates. Common question: 'design the real-time content feed system that ranks 100 candidate posts for a user based on their watch history and explicit follows, with a response time under 200ms'. Round 3 (Technical Interview 2 or ML Round): For ML engineer candidates, a deep dive on recommendation systems, model evaluation, and production ML challenges. 'How would you evaluate whether a new recommendation model is better than the production model for ShareChat's Indic-language user base, when standard English-language evaluation metrics may not apply?' Round 4 (Culture Round): ShareChat values building for the next billion Indian users, many of whom speak regional languages. Mission alignment around digital inclusion is explicitly evaluated.

ShareChat interviews reward candidates with recommendation systems and multilingual NLP knowledge. Two-tower models: the standard architecture for large-scale retrieval. A user tower encodes user history; an item tower encodes content features. Approximate nearest neighbour search (FAISS, ScaNN) retrieves candidate content. ShareChat uses this architecture for its initial retrieval step. Content moderation for low-resource languages: Indic languages like Bhojpuri, Maithili, and Gondi have very limited labelled training data. ShareChat has had to build internal annotation pipelines and use techniques like cross-lingual transfer learning, data augmentation, and active learning. Code-switching: Indian social media users frequently mix Hindi/English in the same post ('bhai kal party thi, amazing fun'). Standard NLP models trained on monolingual data fail on code-switched content. How ShareChat handles this (transliteration, custom tokenisation, multilingual models) is a legitimate interview topic. Content diversity in recommendations: a pure engagement-maximising recommendation system can become an echo chamber. ShareChat has experimented with diversity-preserving re-ranking to show users content from outside their filter bubble.

ShareChat pays competitive salaries for an Indian-founded unicorn. Junior SWE or ML Engineer (0-2 years): Rs 18-32 LPA. Mid-level (2-5 years): Rs 30-55 LPA. Senior SWE or Senior ML Engineer (5+ years): Rs 50-80 LPA. ML engineers typically earn 15-25% more than equivalent SWE roles due to scarcity of Indic NLP expertise. ESOPs are part of the package and have been affected by ShareChat's funding environment, which has been more constrained since 2022. The company has right-sized its team and now operates with a leaner, more focused engineering culture. The ML engineering problems at ShareChat are genuinely world-class in the Indic language NLP domain: if you care about building AI for underserved language communities, ShareChat is one of the best places in India to do that work.

ShareChat serves over 180 million monthly active users across 15 Indian languages, creating content moderation challenges at a scale few Indian companies face. Engineering interviews for trust-and-safety and integrity roles test candidates on designing multi-stage moderation pipelines combining hashed perceptual fingerprinting, image classification models, and human review queues with configurable escalation thresholds. Familiarity with the IT Rules 2021 requirements for significant social media intermediaries — including the mandatory grievance redressal officer, monthly compliance reports, and 72-hour government data request timelines — demonstrates practical domain knowledge. Candidates who understand Indic-language text detection challenges specific to code-mixed Hindi-English content have a clear edge.

Following TikTok's exit from India, ShareChat acquired Moj and built it into one of the top short-video platforms in the country. Engineering interviews for the Moj infrastructure team probe CDN architecture decisions for video delivery to Tier-2 and Tier-3 cities with sub-10 Mbps connections, adaptive bitrate streaming using HLS and DASH, and transcoding pipeline design on AWS MediaConvert and custom FFmpeg workers. Candidates are asked how they would design creator analytics dashboards that process tens of millions of daily video upload events in near real-time. Knowledge of Kafka for event streaming and Flink for windowed aggregations appears frequently in these rounds.

ShareChat's hiring process typically runs across four to five rounds completed within two to three weeks for experienced candidates. The first round is a coding screen on HackerRank or an internal tool, testing standard data structures and algorithm problems at medium difficulty. Two technical rounds follow covering system design and a deep dive into past projects. A hiring manager round focuses on product thinking and team collaboration, and a final HR round covers compensation and joining logistics. Bangalore-based roles are filled faster than remote positions. Referrals from existing ShareChat engineers noticeably accelerate the process and candidates are encouraged to use LinkedIn connections proactively.