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
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Evaluate suitable open-weight and commercially available models for on-device execution.
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Benchmark model performance on target mobile hardware.
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Integrate models into Android applications and system services.
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Optimize inference latency, memory consumption and power usage.
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Evaluate quantization, compression and model distillation techniques.
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Work with CPU, GPU and NPU acceleration.
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Evaluate local model capabilities for different workflows.
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Build hybrid local/cloud model routing mechanisms.
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Identify which tasks can be handled locally and which require cloud inference.
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Work with AI agent engineers to integrate local models into the agent runtime.
Requirements
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Strong ML systems or inference engineering experience.
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Experience deploying and optimizing models.
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Strong Python and C++ skills.
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Understanding of neural network inference.
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Experience with model quantization and performance optimization.
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Understanding of hardware constraints and inference benchmarks.
Nice to have
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Experience with on-device LLMs.
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Qualcomm QNN, MediaTek NeuroPilot or similar acceleration frameworks.
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ONNX Runtime, LiteRT, TensorFlow Lite or ExecuTorch.
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Android NDK.
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Mobile or edge AI deployment.
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Multimodal model optimization.
Originally posted on Himalayas
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