Senior Software Engineer, Decision & Planning Algorithm (Robotics) at Grab

Get to Know the Team The robotics team is a core part of Grab's long-term vision for urban embodied AI. Engineers own the full product lifecycle: co-designing and building hardware, developing control and machine-learning systems, and validating them in the real world and in production fleet operations. The environment is fast-moving and multidisciplinary—software, hardware, and data science work closely together to solve real problems at scale. The team is executing an ambitious growth plan to expand the robot fleet across cities over the coming years, with a focus on efficient, safe, and reliable robot-assisted delivery to help address labor shortages in last-mile logistics. With a base in Singapore and China, the team offers opportunities to work on cutting-edge autonomy, deploy systems in complex urban settings, and shape the future of last-mile logistics. If you care about measurable impact, large-scale systems, and cross-functional engineering, you will find meaningful challenges and strong room for career growth.   Get to Know the Role You will contribute to decision and planning algorithms for indoor and outdoor, full-scenario delivery robots. As a senior engineer, we expect you to: • Develop a deep understanding of planning approaches and where they apply; • Take part in scenario breakdown, algorithm design discussions, and implementation; • Debug issues in simulation and on-vehicle tests, quantify performance after release, and drive continuous improvement.We look for solid theory, strong engineering execution, and consistent, high-quality delivery.   The Critical Tasks You Will Perform • Help design global routing and decision & motion planning systems that are safe and efficient while maintaining ride quality. • Research and advance full-scenario driving on urban public roads and semi-enclosed campus roads, including structured and unstructured roads, signalized and unsignalized intersections, crosswalks, dynamic conditions (congestion, construction), and queueing in large dispatch areas. • Research and advance nonlinear, multi-objective planning in unstructured indoor settings: tight spaces, elevators, and dense pedestrian flows. • Contribute to quantitative metrics and a data closed loop, and expand automated test coverage. • Contribute to data-driven, end-to-end planning R&D and scenario optimization.