About the role
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.