About the role
We are preparing the data foundation for our autonomous-driving work, and we are hiring the engineer who will start it. Today we collect street-level imagery and GPS tracks from vehicle cameras, including our own camera units with NVIDIA edge devices on board, and use them to update maps. Next comes a dedicated survey vehicle with a full sensor set: multi-camera, IMU, LiDAR. You will start with the imagery and map data pipelines we run today, and as the sensor platform arrives, you will build the pipelines that turn its data into training datasets for autonomous-driving models.
Build the pipelines that move multi-sensor data (multi-camera video, IMU, LiDAR, GPS) from vehicles into cloud storage, synchronize and geo-reference the streams
Own the storage layout and lifecycle for sensor data: partitioning, hot and archival tiers, cost control as volume grows
Build validation and monitoring on the ingest path: incomplete uploads, missing or unsynchronized streams, vehicles that stopped producing usable data
Prepare training datasets for autonomous-driving models: select data by location, route, and conditions, version datasets, and deliver them in the formats ML teams need
Until then: take over and develop the current imagery and GPS-track pipelines from vehicle cameras, and contribute to geodata pipelines (ingestion from company data, OpenStreetMap, Overture Maps and vendors, conflation, validation, publication)
Work with company data in BigQuery and keep spatial queries fast on large PostGIS and GeoParquet tables