AI Phone - 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.

Originally posted on Himalayas