This role owns the software on an Android device installed behind TVs in bars and restaurants, from the platform build down to kernel drivers and the video and audio paths. A solid candidate has shipped a physical product, worked deeply in AOSP and the Linux kernel on ARM chips, and can debug multimedia and HDMI problems that only appear on certain TVs and cable boxes. The person also sets technical direction and mentors other engineers while staying hands on in the code.

How it fits the AI era
The on-device AI depends on a clean, low latency video and audio pipeline, so this engineer keeps the hardware and platform healthy enough for the models to run well. Much of the work is physical and messy, such as board bring up, HDMI quirks and remote recovery of devices in the field, and that stays human. AI can help with log analysis and routine debugging, but the judgment calls on architecture and hardware behavior remain with the engineer.

How it builds AI experience




The role offers hands on exposure to running models on embedded hardware, since the posting lists NPU or GPU acceleration as a nice to have and the AI runs on the box itself. A candidate can build this by learning how inference is deployed on ARM devices and how video frames move from capture to model input. Understanding the latency and resource limits of on-device AI is the practical way to prepare.

Worth showing

AOSP platform changes shipped

Linux kernel driver work

ARM board bring up

HDMI and AV debugging

Multimedia pipeline latency tuning

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Taiv
An AI system that watches the live TV feed in bars and restaurants and swaps ad breaks for venue content and targeted ads, with the venue sharing the revenue.

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📌 Senior Embedded Systems Engineer (Manitoba)
🏢 present of AI
📍 Manitoba