On-device AI / ML Systems Engineer

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.