全职 Android Software Engineer 工作, 薪水 up to SGD 8,000, TECHKNOWLEDGEY PTE. LTD. Central Region (Singapore) 公司招聘中 - Ricebowl

There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our client's mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.

Their product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior.

Overview:

As an Android Software Engineer, you own the Android client experience, how AI feels, behaves, and performs on mobile devices. This is not a thin client role. You will build a production Android application where AI interactions are core to the product, and performance, reliability, and clarity matter.

Focus

• Build and maintain production Android apps using Kotlin.

• Integrate AI-powered features (chat, vision, voice, recommendations) via backend APIs.

• Design UX patterns for AI interactions, including streaming responses, retries, and partial results.

• Optimize performance, memory usage, and responsiveness for AI-heavy flows.

• Implement analytics, logging, and feedback capture to support AI evaluation and iteration.

• Collaborate closely with backend and ML engineers on API contracts and system behavior.

• Ensure app stability, security, and scalability in production environments.

Ideal Experiences

• 3+ years of Android development experience using Kotlin.

• Hands-on experience integrating AI features (e.g. LLM, vision, speech APIs).

• Strong understanding of asynchronous programming (Coroutines, Flow).

• Familiarity with REST or gRPC APIs and structured data formats.

• Strong debugging and performance profiling skills.

• Comfort building in environments with latency, partial failure, and non-deterministic behavior.

• Experience with MLKit or light on-device inference.

• Published production apps on the Google Play Store.

Outcomes

• Stable, smooth, and reliable real-world use android applications.

• Performance is optimized: responsive, low-latency, and efficient on memory and CPU.

• Production issues are detected early, monitored effectively, and resolved with clear root-cause analysis.

Tech Stack

• Kotlin / Java

• SQL / noSQL

• TensorFlow Lite (on-device inference)