Sensor Fusion & 3D Reconstruction Engineer
- Toronto, ON, Canada
Full-time
Applications open in a new tab.
Job details
- Job type
- Full-time
- Apply by
- Posting language
- English
- Posted
Original job description
Company Description
LIVESENS is building the Urban Nervous System. We combine computer vision, LiDAR, and sensor fusion to turn everyday vehicles into infrastructure intelligence. Our Remora platform captures the physical world and transforms it into actionable data that helps cities inspect and maintain infrastructure.
Role Description
Build the accuracy behind the Urban Nervous System.
We’re hiring a Sensor Fusion & 3D Reconstruction Engineer to turn Remora sensor data into repeatable 3D maps for reliable infrastructure measurements.
Working directly with the founders, you’ll own the calibration, positioning, and reconstruction pipelines that make those measurements possible. Target centimeter level accuracy, measure the error, and define the limits.
This is a full time contract position for approximately six months, with a hybrid work arrangement in Hamilton, Ontario. Start ASAP.
What You Will Own
- RTK GNSS and IMU fusion for vehicle trajectories
- Sensor calibration, time synchronization, and motion compensation
- Registration across repeated passes and dense 3D reconstruction
- Dynamic object removal and validation against ground truth
What You Bring
- Strong understanding of 3D geometry, ICP, and Kalman filtering or factor graph estimation
- Practical experience with RTK positioning and real sensor calibration
- Proficiency in Python, ROS2, and Open3D or PCL
- AI coding tools in your daily workflow
- A disciplined approach to measuring accuracy and understanding failure cases
- Experience with C++, GTSAM, Ceres, or mobile mapping is a plus.
More about this job
Responsibilities
Own calibration, positioning, and reconstruction pipelines that transform vehicle sensor data into repeatable 3D maps for infrastructure measurement. Develop and validate trajectory fusion, sensor synchronization, motion compensation, repeated-pass registration, dense reconstruction, and dynamic-object removal, while measuring accuracy against ground truth.
Requirements
Candidates should understand 3D geometry, ICP, and Kalman filtering or factor graph estimation, and have practical experience with RTK positioning and real sensor calibration. Proficiency in Python, ROS2, and Open3D or PCL is required, along with a disciplined approach to accuracy measurement and failure analysis; C++, GTSAM, Ceres, or mobile mapping experience is a plus.
Skills
- Sensor Fusion
- 3D Reconstruction
- RTK GNSS
- IMU Fusion
- Sensor Calibration
- Time Synchronization
- Motion Compensation
- 3D Geometry
- ICP
- Kalman Filtering
- Factor Graph Estimation
- Python
- ROS2
- Open3D
- PCL
- Accuracy Validation
Visa sponsorship
Not detected in the job text
Categories
- Engineering
- Technology
- Software
- Data & Analytics
Keywords
- Sensor Fusion
- 3D Reconstruction
- Urban Infrastructure
- Computer Vision
- LiDAR
- RTK GNSS
- IMU
- Vehicle Trajectories
- Sensor Calibration
- Time Synchronization
- Motion Compensation
- Point Cloud Registration
- ICP
- Dense 3D Mapping
- Dynamic Object Removal
- Ground Truth Validation
- 3D Geometry
- Kalman Filtering
- Factor Graph Estimation
- Python
- ROS2
- Open3D
- PCL
- C++
- GTSAM
- Ceres
- Mobile Mapping
- Accuracy Measurement