About this role
Build and own Life360's on-device ML platform for IoT trackers and wearables. Architect reusable inference frameworks running on resource-constrained RTOS firmware, deploy quantized ML models on Cortex-M-class hardware, and lead embedded ML across the full device software stack from sensor to cloud.
Must have
10+ years of firmware engineering shipping complex consumer hardware at scale
Bachelor's degree in Electrical Engineering, Computer Science, or related field
Deep C/C++ for embedded systems with real fluency in RTOS internals
Strong low-level hardware skills including SPI, I2C, UART, DMA, interrupts, and driver development
Demonstrated experience deploying ML models on microcontroller-class hardware in shipping products
Hands-on with embedded inference frameworks such as TFLite Micro, CMSIS-NN, or ExecuTorch
Experience with model optimization techniques including quantization and pruning
Solid grounding in sensor data and signal-processing pipelines such as IMU
Daily use of AI coding tools as a genuine development partner for firmware and ML work
Technologies
Responsibilities
Design and build reusable on-device inference framework for any Life360 device to adopt
Architect runtime, model integration path, and sampling and preprocessing pipeline
Make platform-level calls on runtime and model format, memory and flash budgeting, and OTA model updates
Integrate inference into resource-constrained RTOS firmware without compromising stability or power
Own low-level plumbing including drivers, DMA data paths, SPI, I2C, and middleware
Debug on real hardware using oscilloscope, logic analyzer, and JTAG
Develop and ship models on device handling quantization, operator support, and latency tradeoffs
Squeeze inference into tight power, memory, and latency envelopes validated in real world
Drive alignment across firmware, app, cloud, data science, hardware, and ops teams
Raise team's embedded-ML fluency through code review, design docs, and pairing
Carry regular firmware work when ML demand is light including features, bugs, and on-call
Benefits
Recruitment process
Reports to Engineering Manager, Connected Devices
Works alongside firmware, app, cloud engineers, data science, hardware, operations, and data teams
Connected Devices team owns end-to-end device software readiness across full portfolio
AI-Native engineering team using AI across specifications, code, test, review, data analysis and triage
Role is foundational with no reusable platform yet behind first on-device ML feature
Year one success includes 2-3 on-device ML features shipped to Pet GPS fleet
Canada job title will be Developer in lieu of Engineer
Remote First company with remote work as primary experience for all employees
