Staff Firmware Engineer, AI Native, Edge ML
Job description
About the role
This role defines and builds Life360's on-device machine learning platform, enabling the full device software stack to run intelligent features at the edge. It combines deep embedded firmware ownership with hands-on Edge ML deployment on constrained hardware.
Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.
Key facts
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Location: Remote (USA; Remote, Canada)
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Engagement: Hybrid by design, both halves are non-negotiable
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Compensation: $143,000 to $261,500 USD; $207,000 to $242,500 CAD
- Degree requirement: Bachelor's in Electrical Engineering, Computer Science, or related field
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Team: Connected Devices, remote-first company with over 500 remote employees
- Years: 10+ years of firmware engineering experience
- Visa: Open to US and Canadian candidates
What you'll do
Own the full firmware implementation and debug path, from architecture to mass-production field issues, using oscilloscopes, logic analyzers, and JTAG to resolve cross-layer problems.
Define and implement the path from sensor data and signal processing to model inference on Cortex-M-class hardware.
Requirements
- 10+ years of firmware engineering experience shipping complex consumer hardware through prototype to mass production.
- Bachelor's degree in Electrical Engineering, Computer Science, or a related field.
- Deep proficiency in embedded C/C++ and real-time operating systems such as Zephyr or FreeRTOS.
- Strong low-level hardware skills including SPI, I2C, UART, DMA, interrupts, and driver development, with hands-on debugging using scopes and JTAG.
- Demonstrated experience deploying ML models on microcontroller-class hardware in a shipping product, not a course project or PoC.
- Hands-on work with embedded inference frameworks such as TFLite Micro, CMSIS-NN, or ExecuTorch, and model optimization techniques like quantization and pruning.
- Solid grounding in sensor data and signal processing pipelines that feed on-device models.
- Daily use of AI coding tools such as Claude Code or equivalent, reviewing AI-generated code and AI-assisted model outputs as critically as human-written code.
- Strong written communication and the ability to work across firmware, hardware, and data science.
Nice to have
- Security and compliance experience for connected devices, including secure boot, key provisioning, signed and rollback-safe OTA, and RF/regulatory certification.
- Hands-on with cellular, BLE, GPS/GNSS, or audio subsystems on battery-powered wearables or trackers.
- New-board bring-up experience, including powering unproven hardware and verifying power rails and peripherals.
- Hardware schematic evaluation and design review skills.
- Factory and manufacturing support experience, including test development and production-line debug.
Skills & tools
General-purpose firmware toolchains, RTOS platforms, sensor and signal-processing pipelines, and Edge ML frameworks such as TFLite Micro, CMSIS-NN, or ExecuTorch. AI-assisted coding tools such as Claude Code or equivalent are used daily.
Practical notes
- Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
This is a foundational staff role shaping an on-device ML platform for a large-scale consumer device fleet. Success requires ownership across firmware, ML, and production, balancing accuracy, power, and memory constraints at scale. The role emphasizes AI-native workflows, real-world optimization, and cross-team collaboration in a remote-first environment.