Senior Cloud Infrastructure Engineer Big Data & ML Platform (Autonomous Driving)

We are building full-stack autonomous driving systems for commercial vehicles, and we are standing up our overseas cloud team in Singapore. This is an early, high-ownership role: you will help architect the cloud, data, and ML platform that powers our global autonomous-driving program.

Autonomous vehicles generate enormous amounts of multi-modal data — camera, LiDAR, and radar — and every improvement to the driving stack depends on how fast and reliably we can ingest, process, and learn from it. You will build the data and ML backbone that turns petabytes of fleet data into better models, and establish Singapore as our overseas

data and compute hub.

What You'll Do

• Architect and operate the cloud infrastructure and data platform for our overseas autonomous-driving program.
• Design petabyte-scale ingestion, storage, and processing for multi-modal fleet data (camera / LiDAR / radar, logs, telemetry).
• Build big-data pipelines (e.g., Spark / Flink / Ray / Kafka) powering data mining, triage, and the auto-labeling and data-engine workflows.
• Build and run ML infrastructure / MLOps: distributed GPU training, orchestration, experiment tracking, model registry, and CI/CD for models.
• Support large-scale simulation and evaluation compute for the autonomy stack.
• Own reliability, observability, performance, and cost efficiency at scale, with infrastructure-as-code and strong operational practices.
• Establish data governance, security, and cross-border data handling for overseas data, in line with Singapore PDPA and our data-isolation requirements.
• Collaborate closely with perception, planning, data, and platform teams — including counterparts across regions

What We're Looking For

• BS or MS in Computer Science, Engineering, or equivalent practical experience.
• Solid experience building cloud infrastructure on AWS, GCP, or Azure.
• Hands-on experience with big-data technologies — e.g., Spark, Flink, Kafka, and data lake / lakehouse formats (Iceberg / Delta / Hudi).
• Experience with ML infrastructure / MLOps — Kubernetes and containers, distributed / GPU training, workflow orchestration (Airflow / Kubeflow / Ray), and model serving.
• Strong programming in Python plus one of Go / Java / Scala, with solid software-engineering fundamentals.
• Experience with infrastructure-as-code (Terraform), CI/CD, and observability.
• Strong grasp of distributed systems, scalability, performance, and cost.
• Based in Singapore, with valid authorization to work in Singapore.

Nice to Have

• Autonomous driving, robotics, or other large-scale sensor / multi-modal data experience.
• Petabyte-scale data platform or data-lake experience.