Grab
Technology
LeadLocalizationEngineer(L4AutonomousSystems)
Neural analysis suggests this role is
optimal for mid candidates.
“Lead Localization Engineer (L4 Autonomous Systems) at Grab. Skills: SLAM, Localization, Robotics, Autonomous Systems, C++, Linux/ROS 2, Sensor Fusion. Design and implement localization strategies matching real-time sensor data against High-Definition (HD) Vector Maps. Lead the development of SLAM algorithms for large-scale, semi-structured environments”
What You'll Achieve.
Driving the system from prototype to large-scale commercial deployment; Delivering highly productive, safe and efficient robot delivery services; Supporting rapid operational expansion; Enhancing overall motion intelligence
Industry & Context.
Resolve localization ambiguity in repetitive scenes; Resolve localization ambiguity in repetitive scenes; Performance bottleneck resolution
Working onsite at Grab office
What They're Looking For.
Must Have
Master's degree or above in CS, Robotics, Automation, Geomatics, or related fields, 5+ years of SLAM R&D experience in robotics or autonomous full-cycle experience in at least one mass-produced product, Expert in high-performance C++, Proficient in Linux/ROS 2, Practical experience in SLAM acceleration using CUDA or TensorRT, Hands-on experience with tightly-coupled LIO-VIO systems, Expertise in GNSS/RTK + INS tight-coupling and HD Map-based localization, Ability in system architecture design, module refactoring, and performance bottleneck resolution, Demonstrated proficiency in leveraging AI tools and systems to drive efficiency and innovative problem-solving, Understanding of State Estimation Integrity (Protection Levels/EPE) for safety-critical navigation
Nice to Have
Kubernetes a plus
What You'll Do.
Design and implement localization strategies matching real-time sensor data against High-Definition (HD) Vector Maps
Lead the development of SLAM algorithms for large-scale
semi-structured environments
Develop SLAM algorithms focusing on tightly-coupled localization and mapping architectures using LiDAR
Overcome unique last-mile challenges: filtering dense dynamic obstacles
resolving localization ambiguity in repetitive scenes
ensuring seamless Global Navigation Integrity
Maintain decimeter-level accuracy in "Urban Canyons" and under high-speed GNSS-denied conditions
Build SLAM solutions optimized for low-cost hardware
Ensure long-term map stability
Overcome unique last-mile challenges: filtering dense dynamic obstacles
resolving localization ambiguity in repetitive scenes
ensuring seamless indoor-outdoor transitions
Maintain stable centimeter-level localization in GNSS-denied areas
Lead the deployment on NVIDIA Orin
focusing on deterministic
low-latency output and Functional Safety (SOTIF) principles
Refactor code architecture for memory and power consumption management
Coordinate with hardware teams on sensor selection
and temporal synchronisation
Design and implement automated toolchains for map generation
Build performance evaluation frameworks to drive continuous algorithm evolution via data loops
Collaborate with Perception and Planning/Control teams to provide high-quality
low-latency pose estimation and semantic map data
How You'll Work.
Team & Collaboration
Collaborate with hardware teams on sensor selection, calibration, and temporal synchronisation; Collaborate with Perception and Planning/Control teams to provide high-quality, low-latency pose estimation and semantic map data
Full Job Description
About Grab and Our Workplace Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility. About the Team: The Robotics Technology team is a core part of Grab's long-term vision to build urban embodied AI. Our engineers take full ownership of the product lifecycle: designing and manufacturing hardware in-house, developing control and machine‑learning systems, and rigorously testing in real-world conditions and production fleet operations. This is a fast-moving, multidisciplinary environment where software, hardware and data science experts collaborate to solve practical challenges at scale. We are executing an ambitious growth plan to expand our robotics fleet across cities over the coming years, and we are focused on delivering highly productive, safe and efficient robot delivery services that help address current delivery labor shortages. Based in Singapore and China, we offer opportunities to work on the latest autonomy, deploy solutions in complex environments, and directly influence the future of last‑mile logistics. If you're excited by tangible impact, large-scale systems and cross-functional engineering, you'll find meaningful challenges and rapid career growth here. Get to know the Role: As the core architect of "Spatial Intelligence" for our road-legal, high-dynamic autonomous platforms , you will lead automotive-grade SLAM technologies and the R&D of high-precision, robust SLAM optimized for automotive-grade reliability on mass-market hardware. Your work will directly determine the robot's ability to navigate autonomously and ensure safety at urban road speeds (e.g., residential areas
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