Staff Firmware Engineer, AI Native, Edge ML
New
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Life360Consumer Electronics
All positions, unless otherwise specified, can be performed remotely (within the US and Canada)Full-TimeStaff
SalaryFor 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.
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Job Details
- Experience
- 10+ years
- Required Skills
- Embedded SystemsMachine LearningC++
Requirements
- 10+ years of firmware engineering experience shipping complex consumer hardware at scale.
- Bachelor's degree in Electrical Engineering, Computer Science, or a related field.
- Deep expertise in C/C++ for embedded systems.
- Fluency in RTOS internals such as Zephyr or FreeRTOS.
- Strong low-level hardware skills (SPI, I²C, UART, DMA, interrupts).
- Hands-on debugging experience with oscilloscopes, logic analyzers, and JTAG.
- Demonstrated experience deploying ML models on microcontroller-class hardware in a shipping product.
- Experience with embedded inference frameworks like TFLite Micro, CMSIS-NN, or ExecuTorch.
- Proficiency in model optimization techniques such as quantization and pruning.
- Familiarity with sensor data and signal-processing pipelines (e.g., IMU).
- Daily experience using AI coding tools like Claude Code or Cursor for firmware and ML workflows.
- Strong written communication and documentation habits.
Responsibilities
- Design and build the reusable on-device inference framework including runtime, model integration, and sampling/preprocessing pipelines.
- Own the on-device ML platform end to end, from architectural design to field debugging and root-cause analysis.
- Integrate inference into RTOS firmware (Zephyr / FreeRTOS) while managing power, scheduling, and stability.
- Perform core firmware engineering tasks including driver development and handling low-level hardware communication (SPI/I²C, DMA).
- Develop, quantize, and optimize ML models for execution on microcontroller-class hardware.
- Optimize inference to meet strict battery, memory, and latency constraints.
- Collaborate with cross-functional teams to drive on-device intelligence architecture and raise team technical fluency.
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