Lead Robotics & Hardware Engineer

CNTXT AI; Abu Dhabi; On-site; AED 300,000 - 560,000; Full time

Job summary

CNTXT AI is seeking a senior hardware engineer to own everything on the hardware and robotics side, including platforms, sensors, and capture rigs that produce the data. You will hand clean, well-captured data to software and ML teams, standing behind the hardware you designed.

Responsibilities include designing teleoperation stations for warehouse and lab collection, developing egocentric capture rigs, and establishing data-quality standards while providing technical leadership across adjacent

Qualifications

• 8+ years in robotics/hardware engineering with hands-on physical systems.
• End-to-end ownership of robotics or data-capture hardware that ran at real scale.
• Experience with multi-modal sensor synchronization and calibration.
• Strong knowledge of robot kinematics, control, and teleoperation.

Responsibilities

• Hardware decisions end-to-end: select, spec, and sign off on robotics and capture hardware.
• Teleoperation systems: design/stand up teleop stations for warehouse and lab collection.
• Egocentric capture rigs: design wearable/head-mounted capture setups with sensor placement and synchronization.
• Data quality at the source: define capture standards and ensure hardware meets them before data reaches software.
• Technical leadership: set hardware/robotics direction and review work across the team.

Skills

Robotics

Hardware design

Teleoperation

Sensor selection

Kinematics

ROS/ROS2

Multi-modal sync

Tools

ROS/ROS2

Job description

The role

We build large-scale datasets to train humanoid robots and embodied AI — via teleoperation setups (including warehouse environments) and egocentric human-motion capture. We need a senior engineers who can own everything on the hardware and robotics side: the platforms, sensors, and capture rigs that produce the data, and the decisions about what to build and buy. Software, data pipelines, and MLOps are handled by dedicated engineers — your job is to hand them clean, well-captured data from hardware you designed and stand behind.

What you'll own

• Hardware decisions, end to end. Select, spec, and sign off on all robotics and capture hardware — humanoid and manipulator platforms, whole-body controllers, and the full egocentric stack (head-mounted cameras, VR headsets, motion capture, EMG and other wearables). You make the build-vs-buy calls.
• Teleoperation systems. Design and stand up teleop stations for warehouse and lab collection — rigging, sensing, real-time control, and the operator experience.
• Egocentric capture rigs. Own the design of wearable/head-mounted capture setups: sensor placement, field-of-view and coverage, calibration, and multi-modal time synchronization at the hardware level.
• Data quality at the source. Define capture standards (sync tolerances, FOV/coverage, command smoothness) and make sure the hardware meets them before data reaches the software team.
• Technical leadership. Set the hardware/robotics direction, review work, and be the senior voice the team relies on — including adjacent areas like RF/SDR and signal processing.

What we're looking for

The strongest signal is demonstrated depth, not a year count — someone who has personally built and operated real robot and capture hardware in the field and owned the messy parts (calibration drift, sync offsets, hardware failing mid-collection).

• 8+ years in robotics/hardware engineering (or equivalent depth), hands-on with physical systems — not simulation alone.
• Proven end-to-end ownership of robotics or data-capture hardware that ran at real scale.
• Strong in robot kinematics, control, and teleoperation, and comfortable specifying sensors, cameras, and capture rigs.
• Hands-on with multi-modal sensor synchronization and calibration.
• ROS / ROS2 and the surrounding robotics tooling.
• Evidence of technical leadership — setting direction and raising standards.

Nice to have

Humanoid platforms (e.g. Unitree) and whole-body control egocentric / VR capture (PICO, Quest) and wearables (EMG) NVIDIA Isaac Sim / Isaac Lab / GR00T imitation learning / VLA models DSP, signal processing, or RF/SDR.