
Senior
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.
走进这场面试
免费 · 一场按这个岗位校准的真语音模拟
这场面试考什么
- 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)
常见提问方向
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
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