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CNTXT AI in Abu Dhabi (on-site) is seeking a hands-on Hardware Tech Lead to own the hardware end-to-end for egocentric capture and wearable sensors. You will specify capture hardware, manage vendors, and design hardware IP while guiding engineers and contractors.
You will review schematics, develop firmware for sensor capture, ensure timing precision, power budgeting and reliability, and mentor the hardware team as CNTXT scales.
CNTXT (AI71) | Abu Dhabi, on-site | Full-time | Reports to: Director of AI & Engineering
Role summary
CNTXT (AI71) builds egocentric and teleoperation data infrastructure for humanoid robotics and embodied AI.
This role owns our hardware end to end. You will spec our egocentric capture suite, select and manage vendors, and design hardware hands-on. You will also build CNTXT's own hardware IP. This is a hands-on tech lead role: you set the technical direction for hardware and guide the engineers and contractors working on it.
Key responsibilities
Ego capture suite: specification and architecture
Vendor evaluation and management
Hands-on hardware design
Hardware IP
Technical leadership
Technical skills required
“Must” means we won't hire without it; “Strong” means we expect solid working depth.
Mixed-signal schematic design, component selection, power trees, signal integrity for high-speed links
PCB — Must
Owning or rigorously reviewing multi-layer layouts; DFM/DFT awareness; working with fab and assembly houses
Inertial and pose sensing — Must
IMU selection and integration, sensor fusion basics, intrinsic and extrinsic calibration, drift characterisation
Hardware triggering, PTP/shared clocks, timestamping at source, measuring and bounding cross-sensor offset
Embedded firmware — Must
C/C++ on MCUs and SoCs; RTOS or embedded Linux; drivers for I2C, SPI, UART, USB; DMA and buffering
Low-power and wearable design — Must
Battery selection and management, power budgeting, thermal limits for head-worn devices
Lab and debug — Must
Oscilloscopes, logic analysers, power analysers; systematic board bring-up and root‑cause debugging
Wireless and data offload — Strong
Wi-Fi/BLE integration, high-throughput local storage, reliable offload of large video datasets
EMG or similar analog front ends: filtering, noise, electrode interfaces, sampling
Mechanical collaboration — Strong
Reading CAD, enclosure trade-offs, ergonomics for all-day wear, working with ID/ME partners
Certification — Strong
CE, FCC, RoHS, battery transport rules, UAE TDRA type approval
Scripting — Strong
Python for test automation, data validation and sensor characterisation
Tools — Strong
Altium or KiCad; version control for hardware; a PLM or BOM tool
Experience and leadership
Nice to have