FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.
About the Job
As a Data Platform Engineer, Data Pipelines, you will design and build the systems and integrations that help move data reliably from robots in the field to the teams and services that depend on it — analytics, autonomy, ML training, and deployment operations. You will collaborate with cross-functional teams spanning robotics, autonomy, and deployment to build the data backbone of a field robotics company.
About the Data Platform Team
Every robot we deploy generates a continuous stream of telemetry, sensor logs, and operational data from environments around the world. The Data Platform team builds the systems that capture every autonomy intervention, anomaly, and operational signal from globally deployed robots, classify them, and turn them into the ranked problem list that drives our engineering roadmap.
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.
Experience with edge computing or intermittently connected data collection.
Experience with dbt or similar transformation frameworks.