Staff Firmware Engineer, AI Native, Edge ML

About the On-Device ML Platform:

• Design and build the reusable inference framework that any Life360 device can adopt, including runtime, model integration, and sampling pipeline.
• Own the platform end to end: architect, implement, and debug when it misbehaves on a device in the field.

Core Firmware Engineering:

• Integrate inference into RTOS firmware (Zephyr/FreeRTOS) without compromising stability, scheduling, or power.
• Own low-level plumbing — drivers, DMA data paths, SPI/I²C, and middleware — and debug on real hardware with oscilloscope and logic analyzer.

Developing and Shipping Models:

• Get models running within firmware constraints, handling quantization, operator support, and latency/memory tradeoffs.
• Own the full loop from training output to validated on-device behavior, not just deploying someone else's model.

Setting Direction and AI-Native Work:

• Drive alignment across firmware, app/cloud, data science, hardware, and ops on how on-device intelligence gets built.
• Use AI tooling as a genuine development partner and help raise the team's embedded-ML fluency through code review and pairing.