Get to Know the Team
Our team develops production-grade robotics and autonomy capabilities for the uniquely complex, unstructured urban environments of Southeast Asia. We are ambitious and pragmatic: advancing perception, planning, and control step by step, with safety evidence as the gate for every milestone. We focus on building robust system capabilities, scaling with uncompromised quality, and collaborating with industry leaders while investing in in-house expertise where it differentiates us.
We are a senior, hands-on engineering group that prizes operational excellence, clear interfaces, and reproducible pipelines.
Get to Know the Role
As the Principal Motion Planning Engineer, you will own the behavioral and motion planning stack for Grab's autonomous platform.
You will architect, deliver, and continuously improve a production-grade motion planning system that integrates constrained trajectory optimization with learning-based models for interaction-aware decision-making in complex urban environments.
You will lead closed-loop validation and ML experimentation across simulation and real-world testing to measurably improve planner robustness, safety, and performance.
This is a hands-on technical leadership role with domain ownership of planning. You will report to the Head of Engineering.
The Critical Tasks You Will Perform
Technical Direction & System Architecture
- Define and evolve the technical direction for the motion planning stack, spanning behavioural planning and trajectory generation.
- Architect a scalable, production-grade planning stack tailored to the challenges of Southeast Asian environments.
- Ensure coherent integration of planning with upstream perception/prediction signals and downstream execution layers, balancing architectural clarity with system performance.
- Lead technical design reviews within the planning domain, ensuring robustness, safety alignment, and production readiness.
Hands-on Development & Implementation
- Lead the development of real-time motion planning algorithms, including behaviour selection, constrained trajectory optimization, and interaction-aware decision-making.
- Design, integrate, and evaluate learning-based planning components in real-time systems, ensuring measurable improvements in closed-loop performance.
- Drive data-driven experimentation using logged and simulated data to improve planner behaviour and robustness.
- Lead the lifecycle of planning components: specification, design, implementation, testing (simulation, SIL/HIL), and deployment.
Cross-Functional Collaboration & Technical Influence
- Mentor engineers within the planning domain and improve for design rigor, experimentation discipline, and production readiness.
- Partner with Perception, Prediction, Mapping, and Control to ensure coherent system behaviour and trajectory feasibility.
- Help build a culture of data-driven iteration and technical excellence across the autonomy stack.
