Robotics Engineer

Pentangle Tech Services | P5 Group; Illinois; On-site; USD 90,000 - 130,000; Full time

Job summary

Pentangle Tech Services | P5 Group is seeking a robotics localization engineer in Illinois to develop and productionize 2D LiDAR localization algorithms using C++ and ROS2. You will improve SLAM, state estimation and sensor fusion for autonomous systems and integrate multi-sensor pipelines in real-world environments.

The role emphasizes algorithm development, sensor calibration, performance metrics, and cross-team collaboration to deliver production-ready localization and mapping solutions.

Qualifications

• 2–6 years of professional experience in robotics, autonomous systems, perception, localization, or related fields.
• Strong programming experience in C++.
• Hands‑on experience with ROS2.
• Strong understanding of LiDAR‑based localization and SLAM.
• Experience with state estimation and sensor fusion.
• Experience with Particle Filter, Factor Graph Optimization, LiDAR/IMU/GPS/UWB sensors.
• Experience developing algorithms for autonomous robots or vehicles.

Responsibilities

• Develop and implement production-grade 2D LiDAR localization algorithms using C++ and ROS2.
• Develop and improve localization, SLAM, and state-estimation solutions for autonomous robotic systems.
• Build and evaluate algorithms using Particle Filters, Kalman Filters, and Factor Graph Optimization.
• Develop multi-sensor positioning and fusion solutions using LiDAR, IMU, GPS, and UWB.
• Perform sensor calibration and integrate data from multiple sensors for reliable localization.
• Develop perception and mapping solutions for autonomous systems.
• Analyze localization quality and implement metrics to support navigation and system decisions.
• Troubleshoot algorithm and sensor integration issues in real-world robotic environments.
• Collaborate with robotics, perception, controls, and software teams to integrate algorithms into production systems.

Skills

C++

ROS2

LiDAR-based localization

SLAM

State estimation

Sensor fusion

Particle Filter

Kalman Filter

Factor Graph Optimization

LiDAR/IMU/GPS/UWB sensors

Tools

ROS2

Job description

• Develop and implement production-grade 2D LiDAR localization algorithms using C++ and ROS2.
• Develop and improve localization, SLAM, and state-estimation solutions for autonomous robotic systems.
• Build and evaluate algorithms using Particle Filters, Kalman Filters, and Factor Graph Optimization.
• Develop multi-sensor positioning and fusion solutions using LiDAR, IMU, GPS, and UWB.
• Perform sensor calibration and integrate data from multiple sensors for reliable localization.
• Develop perception and mapping solutions for autonomous systems.
• Analyze localization quality and implement metrics to support navigation, recovery, and system-level decision making.
• Troubleshoot algorithm and sensor-integration issues in real-world robotic environments.
• Collaborate with robotics, perception, controls, and software teams to integrate algorithms into production systems.

Required Qualifications

• 2–6 years of professional experience in robotics, autonomous systems, perception, localization, or related fields.
• Strong programming experience in C++.
• Hands‑on experience with ROS2.
• Strong understanding of LiDAR‑based localization and SLAM.
• Experience with state estimation and sensor fusion.
• Experience with one or more of:
• Particle Filter
• Factor Graph Optimization
• Experience working with LiDAR, IMU, GPS, or UWB sensors.
• Experience developing algorithms for autonomous robots, autonomous vehicles, or similar systems.

Preferred Qualifications

• Experience with computer vision and perception.
• Experience with semantic segmentation and RGB‑D mapping.
• Experience with 3D mapping or reconstruction.
• Experience with advanced perception technologies such as NeRF or 3D Gaussian Splatting.
• Experience with imaging sonar or underwater perception.
• Experience developing multi‑modal perception pipelines combining sonar, vision, GPS, LiDAR, and/or IMU.
• Experience with sensor calibration and challenging real‑world localization environments.