Lead Sensors Engineer

Sabi•San Francisco, CA

21h

About The Position

We are a small team solving one of the hardest problems in human-computer interaction: a noninvasive wearable that turns thought into text, no surgery required. By pairing ultra-high-density neural sensing with our Brain Foundation Model, we decode neural signals with a fidelity once reserved for implants. Our mission is to give a billion people a direct link between mind and machine - expanding how humans think, communicate, and create. We are building a next-generation AI companion wearable powered by EEG/BCI technology. Our device reads and responds to neural signals in real time, creating a deeply personal experience that adapts to each user. This is not another productivity tool or a gadget — it is a new category of technology built around human potential. We are backed by strong investors, moving fast, and assembling a world-class team to bring this to market. If you thrive at the frontier of what’s possible and want to build something that genuinely changes how people interact with their own minds, we want to talk to you. Our first product is a sensor-rich textile beanie that measures the wearer's physiology and surroundings. We are going to market by the end of the year. You will own the full non-EEG sensor system: architecture, hardware, and algorithms. PPG is the primary focus, including the optical front-end, the skin interface, and the algorithms that produce a reliable heart rate and HRV signal during motion. You will also own the rest of the sensor suite, including inertial, body temperature, and environmental sensing, as well as the sensor fusion across them. You report to the Head of Hardware, work regularly with the CEO and CTO, and partner with the electrical, firmware, product design, and reliability leads. Much of the build happens with external engineering teams at our contract manufacturers. The work is outsourced; the accountability is yours.

Requirements

10+ years in sensor or hardware development for consumer wearables, with at least one shipped product where you owned a sensing system end-to-end.
Deep expertise in optical PPG: front-end design and motion-robust heart-rate and HRV algorithms validated in real-world wearable conditions.
Strong analog front-end design background, including noise, signal integrity, and low-power sensing systems.
Experience integrating sensors in wearables: selection, calibration, characterization, and data analysis.
Direct experience owning sensor selection, benchmarking, and BOM, including second-source strategy.
Hands-on in the lab (oscilloscope, logic analyzer), and comfortable partnering with EE, firmware, and ME, and with overseas CMs.

Nice To Haves

Inertial-sensing algorithms: activity, gesture, and context classification, and orientation estimation.
Additional health sensors: SpO₂, ECG, EDA, or EMG.
Contact-based body-temperature measurement: thermal modeling, calibration, and ambient compensation.
Multimodal sensor fusion across optical, inertial, and temperature streams.
Knowledge of human physiology or biomechanics.
Biocompatible skin-contact and dry-contact sensor interfaces.
Always-on, ultra-low-power sensing architectures.
Founding engineer or lead-level experience at an early-stage consumer hardware company.

Responsibilities

Lead the sensor system architecture and R&D for the product's non-EEG sensing.
Design, select, and evaluate the sensor suite, with optical sensing as the primary focus: Optical: PPG and SpO₂ (primary focus) Inertial: accelerometer and gyroscope Body temperature Environmental: ambient temperature, humidity, light, and barometric pressure
Own heart-rate and HRV estimation, including the algorithms that stay accurate while the wearer is in motion.
Drive sensor front-end design and component selection, with BOM ownership and second-source strategy, partnering with the EE lead on schematic and layout.
Develop the signal conditioning, calibration, and characterization methods across the sensor suite.
Define the sensor-fusion approach that combines these streams into reliable signals for the AI assistant.
Specify the electrical, mechanical, and firmware interfaces each sensor needs, partnering with product design on skin-contact placement and optical windows, and with firmware on the acquisition runtime and hardware-aligned cross-sensor timestamping.
Partner with the AI/ML lead on anything that becomes a learned model, and with the reliability lead on skin-contact durability, sweat tolerance, and long-wear stability.
Lead technical reviews and design documentation for the sensing system.
Ship the product by end of year, and build and lead the sensing team.