\n What You'll Get To Do • Design and build the data platform, frameworks, and developer tooling that power ingestion across Field AI. • Handle the realities of field data: intermittent connectivity, large sensor payloads (LiDAR, camera, IMU), edge-to-cloud synchronization, and backfill from offline deployments. • Develop reusable ingestion SDKs, APIs, and services that enable teams to onboard new robotics data sources with minimal custom code. • Build and maintain integrations across heterogeneous sources: robot/edge systems, fleet management and deployment tooling, simulation outputs, and cloud object storage. • Integrate the platform with downstream consumers: BI tools, ML training and evaluation pipelines, labeling systems, and issue tracking. • Develop connectors and APIs (REST/gRPC, webhooks, CDC) so internal teams can feed data in and consume curated datasets reliably. • Own integration reliability end to end: schema contracts, versioning, retries, backfills, and monitoring. • Optimize pipeline performance, scalability, and cost across growing fleet deployments. What You Have • Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. • 3–5+ years of experience in data engineering or backend engineering focused on pipelines and infrastructure. • Strong programming skills in Python and SQL (C++, Scala, or Java a plus). • Production experience with streaming systems (Kafka, Kinesis, Pub/Sub) and orchestration tools such as Airflow or Dagster. • Experience with a modern warehouse or lakehouse (BigQuery, Snowflake, Databricks, Redshift) and cloud object storage at scale. • Experience building integrations across systems: third-party APIs, internal services, and CDC/ELT tooling (Fivetran, Airbyte, Debezium, or custom connectors). • Experience building for data quality: testing, monitoring, lineage, and incident response. • Strong problem-solving skills and ability to work in interdisciplinary teams. The Extras That Set You Apart • Experience with robotics, autonomy, automotive, or other telemetry-heavy operational data (bag files, fleet logs, time-series sensor data). • Familiarity with robotics middleware and log formats such as ROS/ROS2, MCAP, or rosbag. \n