AI/ML Engineer
Role Overview
The engineer owns the perception layer that turns sensor data into a reliable understanding of the environment.
Perception supports navigation, obstacle avoidance, and fleet management; code is expected to run on real vehicles early.
The role works in challenging outdoor conditions such as dust, glare, heat, uneven terrain, and unmapped areas.
Key Skills for This Role
Full Job Posting
About the company
The client is a UAE-based AI and robotics startup building autonomous systems for heavy-duty environments.
The company designs, builds, and deploys intelligent autonomy for enterprise automation in complex, high-risk operations.
Role overview
The engineer owns the perception layer that turns sensor data into a reliable understanding of the environment.
Perception supports navigation, obstacle avoidance, and fleet management; code is expected to run on real vehicles early.
The role works in challenging outdoor conditions such as dust, glare, heat, uneven terrain, and unmapped areas.
What you will do
- Design, build, and deploy perception using camera, LiDAR, and radar inputs.
- Develop detection, tracking, and segmentation for harsh outdoor and industrial settings.
- Build localization and mapping for GPS-denied and unmapped areas.
- Fuse sensor data into a real-time environmental model for the planning stack.
- Optimize models for on-vehicle edge inference under latency and compute constraints.
- Build fleet-data pipelines for collection, labeling, validation, and retraining.
- Feed perception insights into fleet analytics for mission planning and performance monitoring.
- Collaborate with autonomy, controls, and hardware engineers from test track through deployment.
What you bring
- At least 3 years building perception systems deployed on real robots or vehicles, rather than only curated datasets.
- Strong computer vision skills in detection, tracking, and segmentation on live sensor feeds.
- Hands-on experience with SLAM, point cloud processing, and multi-sensor calibration.
- Experience with sensor fusion, including approaches such as Kalman filtering, factor graphs, or learned fusion.
- Strong Python and C++ skills; experience deploying models to edge or embedded GPU hardware.
- Comfort with ROS/ROS 2 and field testing.
Nice to have
- Model compression and optimization, including quantization, pruning, or TensorRT, is beneficial.
- Fleet-data infrastructure and MLOps experience for continuous model improvement is beneficial.
- Exposure to simulation environments for perception testing is beneficial.
Why apply
- Build technology that runs on real vehicles in real missions.
- The startup offers early-stage ownership.
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