AI Phone - On-device AI / ML Systems Engineer
Bjak
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- Location
- United Kingdom
- Area
- Outside Africa — visa sponsorship (according to the posting)
- Contract
- Full-time
- Field
- ActAIealware
- Published on
- 5 November 2026
- Closing date
- Not provided
Information not provided by the source
duration, work arrangement, salary, reference
Source: Arbeitnow · seen on the original site on 7 November 2026 at 21:02 (Paris time) · location given by the source: United Kingdom
Description
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.
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