Staff Firmware Engineer, AI Native - Life360

About Life360

Life360’s mission is to keep people close to the ones they love. Our category-leading mobile app, Tile tracking devices, and Pet GPS tracker empower members to protect the people, pets, and things they care about most with a range of services, including location sharing, safe driver reports, and crash detection with emergency dispatch. Life360 serves approximately 102.4 million monthly active users (MAU) across more than 180 countries.

Life360 has more than 500 remote-first employees. We are a Remote First company, which means a remote work environment will be the primary experience for all employees. All positions, unless otherwise specified, can be performed remotely within the US and Canada.

We are AI Native

We are building an AI native company where AI is an integral part of how we build and operate. AI tool usage during interviews varies by role. You may be asked to demonstrate proficiency with AI tools, discuss how you leverage AI, or complete interview exercises without AI assistance.

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. 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.

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:

• 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.

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.

What You’ll Do

Build and Own the On-Device ML Platform

• Design and build the reusable on-device inference framework any Life360 device can adopt — the runtime, the model integration path, and the sampling and preprocessing pipeline.
• Make platform-level decisions regarding runtime, model format, memory/flash budgeting, and OTA model updates.
• Own the platform end to end: architect, implement, and debug it when it misbehaves on a device in the field.

Core Firmware Engineering

• Integrate inference into resource-constrained RTOS firmware (Zephyr / FreeRTOS) without compromising stability, scheduling, or power.
• Own low-level plumbing: drivers, DMA data paths, SPI/I²C, and the middleware feeding the pipeline.
• Debug on real hardware (oscilloscope, logic analyzer, JTAG) and drive cross-layer issues to root cause across the firmware/hardware boundary.
• Carry regular firmware work when ML demand is light (features, bug fixes, field issues, and triage).

Develop and Ship Models on Device

• Get models running within firmware constraints (handling quantization, operator support, and latency/memory tradeoffs).
• Own the whole loop, from training output to validated on-device behavior.

Optimize for Extreme Constraints

• Squeeze inference into tight power, memory, and latency envelopes, balancing model accuracy, power draw, and footprint in real-world conditions.

Set Direction and Work AI-Natively

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

What We’re Looking For

Foundation

• 10+ years of firmware engineering experience, taking complex consumer hardware from prototype through mass production, with a track record of shipping at scale.
• Bachelor's degree in Electrical Engineering, Computer Science, or a related field.

Firmware Engineering Core

• Deep C/C++ proficiency for embedded systems and strong fluency in RTOS internals (Zephyr, FreeRTOS, or equivalent).
• Strong low-level hardware skills: SPI/I²C/UART, DMA, interrupts, driver development from the datasheet up.
• Hands-on debugging (scope, logic analyzer, JTAG) and strict power/memory discipline.

Edge ML

• Demonstrated experience deploying ML models on microcontroller-class hardware in a shipping product (not just a course project or PoC).
• Hands-on experience with embedded inference frameworks (TFLite Micro, CMSIS-NN, ExecuTorch, or equivalent) and model optimization (quantization, pruning).
• Ability to develop and train models yourself, not just deploy existing ones.
• Solid grounding in sensor data and signal-processing pipelines (IMU and similar).

AI-Native & Communication

• Active use of AI coding tools (Claude Code, Cursor, or equivalent) as a development partner, reviewing output critically.
• Strong written communication and a habit of documenting decisions across firmware, hardware, and data science.

Nice to Have

• Security and compliance for connected devices (secure boot, key provisioning, signed and rollback-safe OTA, RF/regulatory certifications like FCC/CE).
• Hands-on experience with cellular, BLE, GPS/GNSS, or audio subsystems on battery-powered wearables or trackers.
• New-board bring-up experience (verifying power rails, peripherals, and getting first firmware running).
• Hardware schematic evaluation (reading/reviewing schematics, partnering with EE on design reviews).
• Factory and manufacturing support (test development, production-line bring-up, and debugging yield/quality at contract manufacturers).

AI-Native Expectations

• Daily Use: Leverage AI tools (Claude Code, Cursor, etc.) daily for drafting/refactoring code, debugging on-device failures, writing specs, and validating models.
• Judgment & Ownership: Review every line of AI-generated code critically. You are fully accountable for the code that ships.
• Velocity: Use AI to compress timelines from specifications to working prototypes and validated on-device behavior.
• Team Leadership: Share learnings, patterns, and guardrails to raise the team's collective AI fluency.

Our Benefits

• Medical, dental, vision, life, and disability insurance plans (100% paid for US employees; supplemental plans provided for Canadian employees).
• 401(k) plan with company matching (US) / RRSP with DPSP plan (Canada).
• Paid parental leave.
• Mental Wellness Program & Employee Assistance Program (EAP).
• Generous time off: flexible PTO, companywide holidays, plus summer and winter shutdowns.
• Learning & Development programs.
• Equipment, tools, and reimbursement support for a productive remote environment.