Staff Firmware Engineer, AI Native, Edge ML

About The Team

The Connected Devices team at Life360 owns end-to-end device software readiness across our portfolio — firmware, app, and cloud engineering working as one team to ship the trackers and wearables that keep families connected to the people, pets, and things they care about most. We're not just a firmware team: we own the full device software stack, from the hardware modules to the cloud, across the whole lifecycle — architecture and hardware bring-up through mass production and post-launch refinement.

We're moving toward a more intelligent hardware ecosystem, building Life360's first on-device intelligence — processing complex sensor data on the device itself, in real time, within tight power and memory budgets. We're an AI-Native engineering team: AI isn't just a tool we use, it's how we work — across specifications, code, test, review, data analysis and triage. Today that means firmware running across our Tile and Pet GPS tracker lines — devices shipping in the hundreds of thousands of units, each streaming continuous multi-sensor telemetry — which is the scale this on-device ML platform should be built to handle.

This role reports to the Engineering Manager, Connected Devices, and works day-to-day alongside our firmware, app, and cloud engineers, data science, hardware, operations, and data teams.

About The Job

We're looking for a Staff Firmware Engineer to build and own Life360's on-device ML platform — the reusable framework that lets any device in our portfolio sample sensor data, run inference on the edge, and act on it without draining the battery or blowing the memory budget.

This is a hybrid role by design, and both halves are non-negotiable. You are a firmware engineer first — deeply fluent in embedded systems and on-device software, from RTOS internals and driver bring-up to power management and debugging on real hardware. You are an Edge ML specialist second — you know how to get a model running, quantized and optimized, on a Cortex-M-class part with kilobytes to spare, whether it comes from Data Science or you build and train it yourself with modern, AI-assisted tooling.