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Interview questions by company and topic | SysRound

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8 questions

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System designMedium
Dynamic Batching for Synchronous Model Inference on One GPU with a 100-Input Cap
Companies: Anthropic
LLM Inference & Serving · System Design · Technical Tradeoffs for PMs
Last reported Apr 2026
Open workspace
Machine learningMedium
Core algorithms inside a demand-side platform: bidding, prediction, pacing and fraud
Companies: Rakuten
Ads Systems · Technical Tradeoffs for PMs
Open workspace
Machine learningMedium
Explain How Ads Get Personalized: Signals, Targeting Models and Privacy Limits
Companies: Conde Nast
Personalization · Technical Tradeoffs for PMs

Machine learningMedium

Explain how Amazon's recommendation engine picks the products each shopper sees

Companies: Amazon

Recommendation Systems · Technical Tradeoffs for PMs

Machine learningMedium

Explain the Data and Models Behind Google Maps Arrival-Time Estimates

Companies: Google

Geo & Delivery Systems · Technical Tradeoffs for PMs

Machine learningMedium

How Ad Platforms Use Audience, Context and Performance Data to Choose Which Ad to Serve

Companies: Rakuten

Ads Systems · Technical Tradeoffs for PMs

Machine learningMedium

On-Device vs Server Inference: Choosing Where an App's ML Model Runs at Scale

Companies: Google

MLOps & Model Serving · Technical Tradeoffs for PMs

Machine learningMedium

Why LLM Training Is Hard and How to Organize the Team to Hit Time and Cost Targets

Companies: Google

Roadmapping & Planning · Technical Tradeoffs for PMs