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.
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. The value of this role is that you own the whole path, from the sensor register to the inference result, without handing off the hard parts.
“Staff” here means you set the technical direction for on-device ML and you ship it — you architect the platform and write the code that proves it works. ML demand will ebb and flow; when it’s light, you pick up regular firmware work alongside the team. This is a full firmware seat, not an ML-only one — the ML specialization is what you bring on top of being a strong, contributing firmware engineer.
This is a foundational role. We’re shipping our first on-device ML feature now, but there’s no reusable platform behind it yet — you’ll turn that first feature into the foundation the rest of the portfolio builds on, with a lot of room to grow from there. In year one, success looks like owning on-device ML end to end as your domain — you architect the inference platform, ship it to the fleet, and root-cause its production issues from device logs yourself. You’re also a credible firmware engineer across the stack — connectivity, OTA, power, drivers, telemetry — not needing anyone else to own the embedded side. And you’ve turned that platform into member-facing value: 2–3 on-device ML features shipped to the Pet GPS fleet, learning from real device behavior.
For candidates based in the US, the salary range for this position is $143,000 to $261,500 USD. For candidates based out of Canada, the salary range for this position is $207,000 to $242,500 CAD. Note: Please be aware that the job title for positions in Canada will be “Developer” in lieu of “Engineer.” We take into consideration an individual’s background and experience in determining final salary; therefore, base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. The compensation package includes a wide range of medical, dental, vision, financial, and other benefits, as well as equity.