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Last checked Nov 4, 2026.
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Upload your resumeWhat the posting discloses
Not stated: Pay, real vacancy, AI screening.
Why these three
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Skills mentioned
About the role
About
We are reimagining the phone. AI phones need to be proactive, intuitive, intelligent and low cost. This is a mission critical for rest of the world to adopt intelligence.
There are over 5 billion people using everyday applications such as email, messaging, notes, tasks, and calendars, yet most are not AI-native. Our mission is to build proactive applications and devices that bring intelligence into daily conversations, coordination and workflows, with minimal prompting.
Responsibilities
- Evaluate suitable open-weight and commercially available models for on-device execution.
- Benchmark model performance on target mobile hardware.
- Integrate models into Android applications and system services.
- Optimize inference latency, memory consumption and power usage.
- Evaluate quantization, compression and model distillation techniques.
- Work with CPU, GPU and NPU acceleration.
- Evaluate local model capabilities for different workflows.
- Build hybrid local/cloud model routing mechanisms.
- Identify which tasks can be handled locally and which require cloud inference.
- Work with AI agent engineers to integrate local models into the agent runtime.
Requirements
- Strong ML systems or inference engineering experience.
- Experience deploying and optimizing models.
- Strong Python and C++ skills.
- Understanding of neural network inference.
- Experience with model quantization and performance optimization.
- Understanding of hardware constraints and inference benchmarks.
Nice to have
- Experience with on-device LLMs.
- Qualcomm QNN, MediaTek NeuroPilot or similar acceleration frameworks.
- ONNX Runtime, LiteRT, TensorFlow Lite or ExecuTorch.
- Android NDK.
- Mobile or edge AI deployment.
- Multimodal model optimization.
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