Global Mapping Engineer – SLAM

Jobtailor; Zürich; Vor Ort; CHF 120.000 - 160.000; Vollzeit

Zusammenfassung

Jobtailor is seeking an advanced mapping/SLAM specialist to design georeferenced mapping for autonomous heavy machinery operating in dynamic construction environments. You will build scalable pipelines across GNSS, LiDAR, cameras, and IMUs to keep site representations current.

The role focuses on robust state estimation, calibration, and real-time performance with an emphasis on production-quality software and rigorous validation. Zürich-based, on-site collaboration is expected.

Qualifikationen

• Master’s or PhD in Computer Science, Robotics, Mechanical Engineering, Electrical Engineering, Geomatics, or a related field.
• 3+ years of experience developing mapping, SLAM, localization, or state estimation systems for real-world robotic platforms.
• Strong understanding of coordinate frames, calibration, sensor synchronization, uncertainty modeling, and real-time robotics systems.
• Experience building multi-sensor mapping pipelines using GNSS, LiDAR, cameras, IMUs, and other sensor data.
• Strong experience with mapping and SLAM algorithms such as LiDAR-inertial odometry, pose graph optimization, loop closure, scan matching, map alignment, and georeferencing.
• Experience writing production-quality C++ and/or Python code in a Linux development environment.
• Experience evaluating mapping and localization performance using clear metrics, datasets, field-testing procedures, and benchmarking frameworks.

Aufgaben

• Design and deploy large-scale georeferenced mapping systems for autonomous heavy machinery operating in continuously evolving construction environments.
• Develop global dynamic mapping pipelines that maintain accurate, up-to-date site representations across evolving terrain, active construction operations, and machine activity.
• Define performance metrics, validation methodologies, and benchmarking frameworks for map quality, localization accuracy, robustness, and runtime performance.
• Develop scalable multi-sensor fusion and SLAM algorithms that enable robust mapping, localization, state estimation, and calibration in challenging outdoor environments with degraded or intermittent GNSS.
• Collaborate closely with multidisciplinary experts to improve the reliability, scalability, and field performance of the overall system.
• Ensure production-quality implementation, documentation, and timely execution in a fast-paced, deployment-driven environment.

Kenntnisse

SLAM

Localization

Mapping

State estimation

GNSS

LiDAR

C++

Python

Linux

Calibration

Real-time systems

Ausbildung

Master’s or PhD in CS/Robotics/Geomatics

Jobbeschreibung

Responsibilities

• Design and deploy large-scale georeferenced mapping systems for autonomous heavy machinery operating in continuously evolving construction environments.
• Develop global dynamic mapping pipelines that maintain accurate, up-to-date site representations across evolving terrain, active construction operations, and machine activity.
• Define performance metrics, validation methodologies, and benchmarking frameworks for map quality, localization accuracy, robustness, and runtime performance.
• Develop scalable multi-sensor fusion and SLAM algorithms that enable robust mapping, localization, state estimation, and calibration in challenging outdoor environments with degraded or intermittent GNSS.
• Collaborate closely with multidisciplinary experts to improve the reliability, scalability, and field performance of the overall system.
• Ensure production-quality implementation, documentation, and timely execution in a fast-paced, deployment-driven environment.

Requirements

• Master’s or PhD in Computer Science, Robotics, Mechanical Engineering, Electrical Engineering, Geomatics, or a related field.
• 3+ years of experience developing mapping, SLAM, localization, or state estimation systems for real-world robotic platforms.
• Strong understanding of coordinate frames, calibration, sensor synchronization, uncertainty modeling, and real-time robotics systems.
• Experience building multi-sensor mapping pipelines using GNSS, LiDAR, cameras, IMUs, and other sensor data.
• Strong experience with mapping and SLAM algorithms such as LiDAR-inertial odometry, pose graph optimization, loop closure, scan matching, map alignment, and georeferencing.
• Experience writing production-quality C++ and/or Python code in a Linux development environment.
• Experience evaluating mapping and localization performance using clear metrics, datasets, field-testing procedures, and benchmarking frameworks.