InMobi / Glance ML Engineer Interview Guide
InMobi (and its consumer brand Glance) runs ML at advertising scale. The interview emphasises CTR prediction, ad targeting, real-time bidding, and personalisation. Closest comparison is to Google Ads ML interviews but with Indian-market context. This guide unpacks the structure and prep.
InMobi's ML organisation runs at advertising scale — billions of ad impressions, real-time auction decisions in single-digit milliseconds, complex multi-objective optimisation balancing advertiser ROI, publisher revenue, and user experience. The interview reflects this.
The loop structure (Senior ML Engineer)
Standard loop: recruiter screen → ML fundamentals + coding → ad ML system design → real-time bidding case → hiring manager.
Round-by-round breakdown
Round 1 — Recruiter screen. Standard.
Round 2 — ML fundamentals + coding. 60 minutes. Conceptual probing with emphasis on calibration (ad CTR predictions feed bid prices, calibration matters more than ranking quality), AUC vs PR-AUC at imbalanced data (CTR is < 5%, often < 1%), and online learning fundamentals (advertising ML retrains frequently).
Round 3 — Ad ML system design. 60 minutes. Common prompts: design a CTR prediction system for display ads, design real-time bidding for ad auctions, design personalised ad targeting. The design has to address feature engineering (user features, ad features, context features, cross features), serving (sub-10ms latency for bidding), and feedback loops (training on logged ad data has bias).
Round 4 — Real-time bidding case. 60 minutes. Real scenario: "your CTR model's calibration shifted overnight. Bids are now 30% too high or 30% too low across the board. Diagnose and fix." Tests the discipline of production calibration monitoring.
Round 5 — Hiring manager. 45 minutes.
What InMobi weights distinctively
1. Calibration depth. Ad bidding is calibration-bound. InMobi probes calibration much more deeply than most Indian unicorn loops. 2. Auction theory awareness. Ad ML candidates are expected to understand auction mechanics (first-price vs second-price, bid shading, reserve prices). 3. Online learning patterns. Models retrain on streaming data. Senior candidates are expected to articulate online learning trade-offs.
Top 10 questions InMobi candidates face
1. "Design CTR prediction for display ads. CTR base rate is 0.3%." 2. "Your bid model's calibration shifted. 30% too high or too low. Diagnose." 3. "How does first-price vs second-price auction change your bidding strategy?" 4. "Design real-time targeting. What features can you compute in 10ms?" 5. "Walk through your most impactful ad ML project." 6. "Click-baiting feedback loop: your model learns to recommend click-baity content. How do you detect and fix?" 7. "Online learning vs batch retraining for CTR. Trade-offs." 8. "Eleven types of leakage. Which apply to ad CTR prediction specifically?" 9. "How do you A/B test a bidding model when the bid changes the population of users you serve?" 10. "Cold start for a new advertiser. What signals do you use?"
The prep path through MSL
For InMobi / Glance:
Compensation
InMobi / Glance ML Engineer in 2026 ranges roughly ₹40 lakh – ₹75 lakh for 5-7 YOE. Staff reaches ₹95 lakh+. InMobi pays competitively for the role family.