How would you design a shared C++ feature that must run efficiently on both Android and iOS?I would isolate platform-independent business and performance-critical logic in a portable C++ core, then expose narrow platform adapters for lifecycle, rendering, storage, networking, and UI integration. I would define clear ownership and threading contracts, use automated unit tests for the shared core, and validate behavior with platform-specific integration tests and profiling.
How do you approach optimizing an on-device LLM for a mobile application?I first establish target latency, memory, battery, accuracy, and device-coverage requirements. I then profile model loading, token generation, CPU/GPU utilization, and memory allocations; evaluate quantization and runtime options; optimize context and KV-cache handling; and benchmark representative workloads across target devices before making quality-performance trade-offs.
What factors would you investigate when an Android graphics feature has intermittent frame drops?I would inspect frame timing, render-thread scheduling, SurfaceFlinger composition behavior, GPU and CPU utilization, shader and texture costs, synchronization points, allocation churn, and thermal throttling. I would use Android tracing and graphics profiling tools to identify whether the bottleneck is application rendering, composition, memory bandwidth, or scheduling, then verify improvements under realistic workloads.
How would you troubleshoot a native C++ crash that occurs only on iOS or only on Android?I would collect symbolicated crash reports, compare platform-specific compiler settings and ABI assumptions, inspect threading and object-lifetime behavior, and reproduce with sanitizers where possible. I would also review platform adapter code, JNI or Objective-C++ boundaries, alignment and memory-management assumptions, and any differences in graphics or network callbacks.
How do you ensure third-party AI SDKs or APIs do not compromise mobile application reliability?I would evaluate the SDK's runtime footprint, privacy model, licensing, update cadence, failure modes, and device compatibility before integration. I would wrap it behind an internal interface, add timeouts and fallbacks, protect calls with robust error handling and telemetry, pin and test supported versions, and validate performance and battery impact across representative devices.
