AI/ML Engineer

Tanqeeb; Dubai; On-site; AED 250,000 - 450,000; Full time

Job summary

Tanqeeb in Dubai seeks an AI/ML Engineer to own the perception stack for autonomous vehicles. You will fuse camera, LiDAR and radar data to create a reliable environment model used by planning and navigation systems.

You will work with Python and C++, deploy models to edge hardware, and collaborate with autonomy, controls and hardware teams to move features from test tracks to real missions.

Qualifications

• 3+ years building perception systems on real robots or vehicles
• Strong CV skills: detection, tracking, segmentation on live sensors
• Hands-on SLAM, point cloud processing and multi-sensor calibration
• Experience with sensor fusion (Kalman filtering, factor graphs or learned fusion)
• Strong Python and C++
• Experience deploying models to edge or embedded GPU hardware
• Comfort with ROS/ROS 2 and field testing
• Ownership mindset

Responsibilities

• Design, build and deploy the perception stack across camera, LiDAR and radar inputs
• Develop object detection, tracking and segmentation that holds up in harsh outdoor and industrial settings
• Build localisation and mapping capability, including operation in GPS-denied and unmapped areas
• Fuse multi-sensor data into a unified, real-time environmental model the planning stack can trust
• Optimise models for on-vehicle inference on edge hardware within strict latency and compute budgets
• Build data pipelines for collection, labelling, validation and retraining from fleet data
• Feed perception insights into fleet-level analytics for mission planning and performance monitoring
• Work closely with autonomy, controls and hardware engineers to take features from test track to deployment

Skills

Perception systems

Python

C++

Computer vision

Sensor fusion

ROS/ROS 2

Ownership mindset

Tools

ROS/ROS 2

Kalman filtering

Point cloud processing

Job description

AI / ML Engineer

Dubai

About the Company:

Our client is an AI and robotics startup building autonomous systems for heavy duty environments. Based in the UAE, they design, build and deploy intelligent autonomy end-to-end, helping enterprise clients bring automation into complex, high risk operations.

The Role

You will own how the vehicles see and understand the world. That means building the perception layer that turns raw sensor data into a reliable picture of the environment in real conditions: dust, glare, heat, uneven terrain and places no one has mapped. Your work feeds directly into navigation, obstacle avoidance and fleet management. This is a hands‑on role with high ownership, and you will be expected to get code running on real vehicles early.

What You Will Do

• Design, build and deploy the perception stack across camera, LiDAR and radar inputs
• Develop object detection, tracking and segmentation that holds up in harsh outdoor and industrial settings
• Build localisation and mapping capability, including operation in GPS-denied and unmapped areas
• Fuse multi-sensor data into a unified, real-time environmental model the planning stack can trust
• Optimise models for on-vehicle inference on edge hardware within strict latency and compute budgets
• Build data pipelines for collection, labelling, validation and retraining from fleet data
• Feed perception insights into fleet-level analytics for mission planning and performance monitoring
• Work closely with autonomy, controls and hardware engineers to take features from test track to deployment

What You Bring

• 3+ years building perception systems that have run on real robots or vehicles, not only curated datasets
• Strong computer vision skills: detection, tracking and segmentation on live sensor feeds
• Hands‑on experience with SLAM, point cloud processing and multi‑sensor calibration
• Experience with sensor fusion (e.g. Kalman filtering, factor graphs or learned fusion approaches)
• Strong Python and C++,
• Experience deploying models to edge or embedded GPU hardware
• Comfort with ROS/ROS 2 and field testing
• Ownership mindset

Nice to Have

• Model compression and optimisation (quantisation, pruning, TensorRT or similar)
• Experience with fleet data infrastructure and MLOps for continuous model improvement
• Exposure to simulation environments for perception testing

Why Apply

• Build technology that runs on real vehicles in real missions
• Early‑stage ownership in a fast‑growing startup