Senior SLAM & Computer Vision Engineer

Senior SLAM & Computer Vision Engineer

Company website : bindassdealdigital.comClassification : Full-time work

Experience : 3 to 7 yearsLocation : On-site/hybrid

Summary

We are building a spatial-mapping device that powers an AI-driven design platform.As a Senior SLAM & Computer Vision Engineer, you will own the spatial intelligence pipeline from multi-sensor data ingestion to accurate 3D room reconstruction.This is a core engineering role that directly defines product success.

Key Responsibilities

• Sensor & SLAM Systems
• Design and implement multi-sensor fusion (RGB, Depth, LiDAR, IMU, IR)
• Integrate and tune SLAM pipelines (RTAB-Map, ORB-SLAM3, LIO-SAM)
• Handle loop closure, drift reduction, relocalization
• Own SLAM performance for indoor spaces
• Calibration & Geometry
• Perform camera intrinsics/extrinsics calibration
• Build robust tf2 transform trees
• Extract spatial geometry from point clouds:

1. walls, floor, ceiling
2. corners & boundaries
3. dimension estimation

• 3D Reconstruction Pipeline
• Generate dense point clouds
• Apply filtering & plane segmentation
• Produce clean, structured 3D room shells
• Export glTF/PLY/OBJ and geometry metadata
• Reliability & Optimization
• Detect and recover from tracking failure
• Optimize compute & power usage on embedded hardware
• Improve system robustness under:

1. low texture
2. glass & reflective surfaces
3. poor lighting

• Collaboration
• Work with hardware team on sensor placement & timing
• Support AI team with accurate geometric outputs
• Guide frontend/3D team on scene structure

Skills

• Core
• Strong C++ and Python
• ROS / ROS2
• tf / tf2
• Multi-sensor SLAM
• Camera geometry & calibration
• Point cloud processing (Open3D / PCL)
• SLAM & CV
• RTAB-Map / ORB-SLAM3 / LIO-SAM
• RGB-D SLAM & LiDAR SLAM
• Feature extraction & tracking
• Pose graph optimization
• Loop closure & drift control
• Tools
• OpenCV
• Eigen
• G2O / Ceres (understanding)
• Linux (embedded preferred)

Nice to have

• Experience with embedded systems (Jetson / RPi)
• TSDF / voxel mapping
• Semantic segmentation
• Experience with interior mapping or robotics

Success Metrics

• Stable SLAM with minimal drift
• Clean room geometry extraction
• Reliable scanning experience for non-technical users

Contact us:

Email: