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

Singapore, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

28 Jul, 26

Salary

0.0

Posted On

29 Apr, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

C++, Python, Linux, ROS, ROS2, Apollo, Autoware, Decision algorithms, Planning algorithms, Machine learning, Reinforcement learning, Imitation learning, CARLA, NVIDIA Isaac, Motion planning, Robotics

Industry

Software Development

Description

Company 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. Job Description 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. Qualifications What Essential Skills You Will Need Master's degree or above in Computer Science, AI, applied math, automotive engineering, or a related field; 3–5 years of R&D experience planning algorithms for autonomy or robotics. Solid C++; proficient Python and Linux; experience with ROS / ROS2 or Apollo / Autoware. Demonstrated proficiency in leveraging AI tools and systems to drive efficiency and innovative problem-solving Algorithms Solid knowledge of classical decision and planning methods (e.g. Lattice, DP/QP, Hybrid A*, POMDP-style approaches). Hands-on experience with joint lateral–longitudinal planning in real scenarios. 3+ years of experience with decision and planning under game-theoretic or interactive settings. Familiarity with ML for planning (e.g. imitation learning, reinforcement learning). Experience innovating for real-world constraints. Domain Familiarity with layered autonomous driving planning stacks. Experience with how upstream modules affect planning and how to measure trajectory quality. Tools & simulation Use CARLA, NVIDIA Isaac (including Isaac Sim / Isaac Lab, etc.) or comparable platforms for algorithm validation. On-vehicle debugging and log analysis. Nice to have Publications at top venues (e.g. ICRA, IROS, IV, CVPR). Experience shipping end-to-end planning to production or field trials. Additional Information Life at Grab We care about your well-being at Grab, here are some of the global benefits we offer: We have your back with Term Life Insurance and comprehensive Medical Insurance. With GrabFlex, create a benefits package that suits your needs and aspirations. Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges. Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours What We Stand For at Grab We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.

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Responsibilities

You will design and implement decision and motion planning algorithms for delivery robots in both indoor and outdoor environments. Additionally, you will debug issues in simulation and on-vehicle tests while driving continuous performance improvements.