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Engineering 🏢 Full Time ⭐️ Terverifikasi

Lead Localization Engineer (L4 Autonomous Systems)

PLT Engineering
Singapore
Estimasi Gaji
SGD 180.000 – SGD 240.000
Live Update
14 Mei 2026
Batas Akhir
14 Mei 2027

Deskripsi Pekerjaan

Singapore is rapidly becoming a global hub for autonomous vehicle technology. PLT Engineering is at the forefront of this revolution, deploying road-legal L4 autonomous platforms. We are seeking an exceptional Lead Localization Engineer to serve as the core architect of our 'Spatial Intelligence' system, solving the hardest challenges in real-world autonomous navigation.

As the Lead Localization Engineer, you will be responsible for the entire lifecycle of our localization stack. This includes leading the design, development, and deployment of automotive-grade SLAM and sensor fusion algorithms. You will own the roadmap for multi-modal sensor calibration, real-time state estimation, and map management, ensuring our vehicles navigate safely and precisely through complex urban environments. Your work will directly bridge cutting-edge research and production-grade software, setting new standards for reliability and performance in autonomous mobility.

You will thrive in this role if you have a deep passion for robotics, a proven track record in delivering production SLAM systems, and strong leadership skills to guide a team of world-class engineers. You will collaborate closely with Perception, Planning, and Systems engineering teams to define requirements and ship features that directly impact the safety and performance of our fleet. This is a unique opportunity to shape the future of urban transportation.

Why Join PLT Engineering?

  • Work on one of the most challenging problems in robotics: L4 autonomous driving.
  • Own the spatial intelligence roadmap for a fleet of real-world autonomous vehicles.
  • Collaborate with a world-class team of roboticists and software engineers.
  • Competitive compensation, equity, and a dynamic work culture in the heart of Singapore.

Tanggung Jawab

  • Architect and implement the core SLAM and localization pipeline for L4 autonomous vehicles, ensuring high accuracy and reliability.
  • Lead the design and integration of multi-modal sensor fusion strategies leveraging LiDAR, Camera, Radar, IMU, and GNSS.
  • Own the end-to-end sensor calibration, synchronization, and degradation monitoring pipeline.
  • Drive the development of robust state estimation algorithms, including visual-inertial odometry (VIO) and graph-based SLAM.
  • Establish rigorous testing and validation frameworks to ensure automotive-grade functional safety (ISO 26262).
  • Mentor and lead a team of talented localization and perception engineers, fostering a culture of technical excellence.
  • Collaborate closely with the Perception, Planning, and Control teams to define system requirements and optimize performance.
  • Stay abreast of the latest research in SLAM, geometric deep learning, and sensor fusion, translating findings into tangible product improvements.

Kualifikasi

  • M.Sc. or Ph.D. in Robotics, Computer Science, Electrical Engineering, or a related field.
  • 8+ years of hands-on experience in state estimation, SLAM, or sensor fusion for autonomous systems (robotics, autonomous vehicles, drones).
  • Deep expertise in multi-view geometry, 3D reconstruction, and probabilistic filtering (EKF, UKF, Particle Filters).
  • Strong proficiency in C++ and Python, with a focus on writing clean, efficient, and well-tested production code.
  • Extensive experience with ROS/ROS2, Eigen, and optimization libraries (Ceres, G2O, GTSAM).
  • Proven track record of deploying real-time SLAM systems in safety-critical, real-world environments.
  • Excellent problem-solving skills and a strategic, product-oriented mindset.
  • Demonstrated technical leadership and mentorship abilities, with excellent communication skills.

Keahlian yang Dibutuhkan

SLAM Sensor Fusion C++ Python ROS State Estimation Visual Odometry LiDAR IMU Kalman Filtering Graph Optimization Autonomous Vehicles Robotics Computer Vision Sensor Calibration

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