Lead Data Infrastructure Engineer

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 closely 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, our team offers opportunities to work on cutting-edge autonomy, deploy solutions in complex urban 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:

We are looking for a Data Infrastructure Engineer to own the data backbone of our robotics programme. Our fleet produces a large, continuous and unforgiving stream of data — sensor recordings, telemetry, autonomy events, and operational and delivery records — and almost every decision the team makes depends on that data arriving complete, on time and demonstrably correct. You will own the pipelines that move it, the validation and monitoring that prove it is trustworthy, the dashboards that engineering, operations and data science work from, and the integration of our robotics data and ML workloads onto Grab's existing data and ML platforms, so the team inherits Grab-scale tooling instead of rebuilding it. This is an ownership role over an established and growing footprint: you will take on existing pipelines, raise their reliability, and shape where the platform goes next.

The day-to-day activities

• Design, build and operate batch and streaming pipelines for robot telemetry, sensor recordings, autonomy events and operational data. • Implement data quality and validation frameworks — schema enforcement, completeness and freshness checks, anomaly detection — with meaningful alerting when data breaks. • Build observability for the pipelines themselves: throughput, lag, cost, failure modes and clear on-call signals. • Build and maintain dashboards and curated, self-serve datasets for engineering, data science and fleet operations. • Integrate robotics data and ML workloads onto Grab's central data and ML platforms — lakehouse, orchestration, feature and model tooling — rather than maintaining parallel stacks. • Design storage, indexing and retention for high-volume sensor data (ROS 2 / MCAP recordings, video, point clouds), balancing accessibility against cost. • Partner with autonomy, perception and ML engineers to define metrics, and make training and evaluation datasets reproducible and versioned. • Own data access, governance, retention and compliance across the regions we operate in. • Drive down platform cost through partitioning, storage tiering, lifecycle policies and compute right-sizing. • Document pipelines and datasets so that consumers can self-serve without a hand-off conversation.