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# AI Phone - On-device AI / ML Systems Engineer

- **Company:** Bjak
- **Location:** London, UK
- **Salary:** £69,197.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Android Software Development, Android NDK, Artificial Neural Networks, C++ (Programming Language), Python (Programming Language), Performance Tuning, Tensorflow, Large Language Models, ONNX (Open Neural Network Exchange) Format, Model Quantization, Machine Learning Operations, Virtual Agents
- **Published:** November 6, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5954029286

## About the Role

* 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.

 * 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.

## Description

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.

 * 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.