Senior SWE, AI Core Capabilities — on-device Gemini Nano for Android

Own end-to-end delivery and optimization of on-device GenAI capabilities on Android, building developer-facing ML Kit and AICore APIs used by first- and third-party app developers, and adapting Gemini Nano (V1–V4) for mobile with a focus on inference latency and resource consumption. Requires 5 years of software development experience, 3 years of Android application development, and 1+ year of large-scale application design/architecture — collaborating directly with DeepMind and CoreML teams.

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What this interview tests

On-device GenAI optimization (latency, memory, inference cost)
Developer-facing API design (ML Kit / AICore)
Android application architecture at scale
Applied GenAI techniques: function calling, RAG, constrained decoding, LoRA fine-tuning
Cross-team collaboration with research/platform teams (DeepMind, CoreML)

Common question themes

Optimize inference latency and resource usage for an on-device model across versions

Design a public API (like AICore) for third-party Android developers

Explain how you'd apply RAG or function calling to a mobile agentic feature

Bridge a high-level API down to low-level hardware acceleration

Navigate a cross-team dependency with a research team like DeepMind

How candidates describe it

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