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Staff Firmware Engineer, AI Native

Life360

Remote, USA; Remote, Canada · Remote · full time · Lead / Staff / Principal

$143,000 – $261,500

Listed on Life360’s own careers site. You apply with them directly — we never stand between you and the employer.

What this role is

Staff-level firmware engineer building Life360's first on-device ML platform for resource-constrained trackers and wearables, bridging embedded systems and edge inference. This role suits experienced firmware engineers who want to own the full stack from sensor to inference result and help establish how AI-native development works in embedded hardware at scale.

Our summary, not Life360’s wording. The full posting is on their site.

Skills this role names

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What they ask for

Required

  • 10+ years firmware engineering from prototype through mass production and shipping at scale
  • Bachelor's degree in Electrical Engineering, Computer Science, or related field
  • Deep C/C++ proficiency for embedded systems and RTOS internals
  • Strong low-level hardware skills including SPI/I2C/UART, DMA, interrupts, and driver development
  • Hands-on debugging with oscilloscope, logic analyzer, and JTAG
  • Demonstrated experience deploying ML models on microcontroller hardware in a shipping product
  • Hands-on with embedded inference frameworks and model optimization techniques
  • Ability to develop and train models yourself, not only deploy them
  • Solid grounding in sensor data and signal-processing pipelines
  • Daily use of AI coding tools as a development partner with critical code review
  • Strong written communication and cross-functional documentation habits

Nice to have

  • Security and compliance expertise for connected devices including secure boot and OTA
  • Hands-on experience with cellular, BLE, GPS/GNSS, or audio subsystems on battery-powered wearables
  • New-board hardware bring-up experience
  • Hardware schematic reading and review with hardware engineering partners
  • Factory and manufacturing support including production-line debug and yield optimization

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