LiDAR Algorithm Expert – Home-Scale Spatial AI Systems
Type: Experienced
Location: Abu Dhabi
Time: February 16, 2026
Position Overview
Origen is building a globally leading home-scale non-visual spatial AI perception system, integrating LiDAR, millimeter-wave radar, and edge AI to enable advanced capabilities including 3D spatial understanding, multi-person localization, behavioral recognition, pointing interaction, and digital twin integration.
The LiDAR Algorithm Expert will take full ownership of the indoor LiDAR spatial perception architecture, driving core algorithm innovation and leading the evolution from MVP to PoC to large-scale residential deployment.
This role represents a critical technical pillar within Origen’s Spatial AI system. The selected candidate will define the performance ceiling, robustness boundary, and real-world deployability of our LiDAR-based home intelligence platform. The position requires deep expertise in 3D perception algorithms, strong engineering judgment, and the ability to translate research-grade solutions into scalable consumer-grade products.
The LiDAR Algorithm Expert will take full ownership of the indoor LiDAR spatial perception architecture, driving core algorithm innovation and leading the evolution from MVP to PoC to large-scale residential deployment.
This role represents a critical technical pillar within Origen’s Spatial AI system. The selected candidate will define the performance ceiling, robustness boundary, and real-world deployability of our LiDAR-based home intelligence platform. The position requires deep expertise in 3D perception algorithms, strong engineering judgment, and the ability to translate research-grade solutions into scalable consumer-grade products.
Key Responsibilities
1. Lead the design and optimization of the full indoor LiDAR perception pipeline, including point cloud preprocessing, spatial segmentation, multi-person detection, tracking, and behavioral modeling.
2. Architect and refine core capabilities such as 3D reconstruction, structural recognition, and dynamic/static object separation to ensure reliable spatial understanding in complex home environments.
3. Develop robust algorithms for multi-person detection and tracking, human pose modeling, pointing recognition, behavioral state analysis, and false positive suppression tailored to residential scenarios.
4. Explore and integrate deep learning approaches to enhance point cloud segmentation, scene understanding, and behavioral recognition where technically appropriate.
5. Drive engineering deployment on edge computing platforms (ARM / GPU / NPU), optimizing latency, memory footprint, power efficiency, and long-term runtime stability.
6. Define and monitor system-level performance metrics including latency, reliability, false positive rate, and cross-layout generalization capability.
7. Collaborate with hardware and product teams on LiDAR selection, installation strategy, and multi-sensor fusion architecture, contributing to overall technical roadmap decisions.
2. Architect and refine core capabilities such as 3D reconstruction, structural recognition, and dynamic/static object separation to ensure reliable spatial understanding in complex home environments.
3. Develop robust algorithms for multi-person detection and tracking, human pose modeling, pointing recognition, behavioral state analysis, and false positive suppression tailored to residential scenarios.
4. Explore and integrate deep learning approaches to enhance point cloud segmentation, scene understanding, and behavioral recognition where technically appropriate.
5. Drive engineering deployment on edge computing platforms (ARM / GPU / NPU), optimizing latency, memory footprint, power efficiency, and long-term runtime stability.
6. Define and monitor system-level performance metrics including latency, reliability, false positive rate, and cross-layout generalization capability.
7. Collaborate with hardware and product teams on LiDAR selection, installation strategy, and multi-sensor fusion architecture, contributing to overall technical roadmap decisions.
Candidate Profile
1. Master’s degree or above in Computer Science, Electrical Engineering, Robotics, Signal Processing, or related fields, with 5+ years of hands-on experience in LiDAR or 3D point cloud algorithms.
2. Strong understanding of full point cloud processing pipelines, including denoising, clustering, 3D reconstruction, and multi-object detection and tracking.
3. Solid knowledge of multi-target tracking approaches such as Kalman-based methods and probabilistic data association techniques.
4. Proven experience working with real-world sensor data and delivering production-level systems in C/C++ and Python.
5. Experience in indoor perception deployment, smart home or IoT systems, LiDAR and millimeter-wave fusion, deep learning for point cloud processing, or edge optimization is highly valued.
6. Demonstrated architectural thinking, problem-solving capability, and ability to collaborate across hardware, algorithm, and product teams.
2. Strong understanding of full point cloud processing pipelines, including denoising, clustering, 3D reconstruction, and multi-object detection and tracking.
3. Solid knowledge of multi-target tracking approaches such as Kalman-based methods and probabilistic data association techniques.
4. Proven experience working with real-world sensor data and delivering production-level systems in C/C++ and Python.
5. Experience in indoor perception deployment, smart home or IoT systems, LiDAR and millimeter-wave fusion, deep learning for point cloud processing, or edge optimization is highly valued.
6. Demonstrated architectural thinking, problem-solving capability, and ability to collaborate across hardware, algorithm, and product teams.
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