ShareChat Senior MLE / Data Scientist Interview Guide
ShareChat operates Indian-language social platforms (ShareChat, Moj) at massive scale. Senior MLE roles cover feed ranking, content moderation, recommendation, creator analytics, and trust & safety. The interview tests recommendation depth, multi-language NLP, and real-time content ranking. This guide covers it.
ShareChat runs ShareChat (Indian-language social) and Moj (short-form video) with hundreds of millions of users. Senior MLE roles work on the feed ranker, content moderation, creator-facing analytics, and trust & safety models. The interview is heavy on recommender systems with a multi-language NLP overlay.
The loop structure (Senior MLE)
Standard loop: recruiter screen → ML fundamentals + coding → ML system design (recommendation) → production case → hiring manager.
Round-by-round breakdown
Round 1 — Recruiter screen. 30 min.
Round 2 — ML fundamentals + coding. 90 minutes. Standard fundamentals with recommendation flavour, plus an NLP angle (multi-language tokenisation, embedding similarity). Coding: LeetCode medium-hard plus a recommendation similarity problem.
Round 3 — ML system design. 75 minutes. "Design Moj's feed ranker" or "design ShareChat's feed ranker." Probed for: candidate gen, multi-task ranking, exploration, cold-start (new creators), diversity, position bias, content moderation integration.
Round 4 — Production case. 60 minutes. Diagnosis-style. "Engagement on Moj feed dropped 8% last week." Tests for systematic diagnosis discipline.
Round 5 — Hiring manager. 45 minutes.
What ShareChat weights distinctively
1. Recommender depth at scale. Moj feeds are ranked in real-time across 100M+ DAU. The infrastructure question is significant. 2. Multi-language NLP. ShareChat supports 15+ Indian languages. Embedding models, content classifiers, and search must work across them. 3. Content moderation. Trust & safety models (NSFW, hate speech, misinformation) are mature and central. Senior MLEs may rotate through this. 4. Creator economy. Recommendations affect creator earnings; creator-facing analytics affect content supply. The two sides are coupled.
Top 10 questions ShareChat senior MLE candidates face
1. "Design Moj's video feed ranker. Goal: long-term engagement, not just session length." 2. "Multi-language embedding strategy — when do you use one shared model vs language-specific models?" 3. "Cold-start: a new creator just joined, how does their content surface?" 4. "Content moderation pipeline: classifier confidence vs human review. Walk through the trade-offs." 5. "Diagnose: engagement on the feed dropped 8% last week. Approach?" 6. "Position bias in feed ranking — explain the problem and your correction strategy." 7. "Design a creator analytics dashboard. What's the leading indicator of creator churn?" 8. "Recommendation feedback loops — how do you prevent the feed from collapsing to a few popular creators?" 9. "Watch-time vs completion-rate vs share — how do you balance these in a multi-task ranker?" 10. "Real-time feature engineering for the feed. Latency budget?"
The prep path through MSL
Common failure modes
Compensation
ShareChat Senior MLE in 2026 ranges roughly ₹40 lakh – ₹70 lakh for 5-7 YOE. Staff roles ₹90 lakh+. Competitive with adtech and SaaS rates.