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 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.
Responsibilities
Design and build the data platform, frameworks, and developer tooling for Field AI ingestion.
Handle challenges of field data, including intermittent connectivity and large sensor payloads.
Develop reusable ingestion SDKs, APIs, and services for onboarding new robotics data sources.
Build and maintain integrations across diverse sources including robotics systems and cloud storage.
Integrate platform with downstream users: BI tools, ML pipelines, and labeling systems.
Develop connectors and APIs (REST/gRPC, webhooks) for data feeding and consumption.
Own integration reliability, including schema contracts and monitoring.
Optimize pipeline performance, scalability, and cost for fleet deployments.
Benefits
Opportunities for career advancement in a cutting-edge tech environment.
Access to the latest technologies and methodologies in data engineering.
Flexible work options to maintain a healthy work-life balance.
Collaborative and interdisciplinary team culture to foster innovation.
Professional development opportunities and resources to enhance skills.
Full Job Description
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.
