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