Senior Software Engineer, Decision & Planning Algorithm (Robotics)

Get to Know 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. 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

You will contribute to decision and planning algorithms for indoor and outdoor, full-scenario delivery robots. As a senior engineer, you will:

• Develop an 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, measure performance after release, and drive continuous improvement.

We look for solid theory, engineering execution, and high-quality delivery.

You will work onsite at Grab office.

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 improve 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.