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Field AI

Data Platform Engineer, Data Pipelines

Irvine, CAfull timemidAdded today

About this role

Field AI seeks a Data Platform Engineer to design and build data pipeline systems that reliably transport telemetry and sensor data from globally deployed robots to internal teams and services. You'll work cross-functionally to create the data infrastructure supporting analytics, machine learning, and operations for autonomous robots operating in demanding environments.

What you'll do

  • Design and build data pipeline systems that reliably move telemetry and sensor data from field robots to downstream consumers
  • Develop integrations between robot systems, data storage, and internal services (analytics, ML training, deployment ops)
  • Classify and structure operational signals, anomalies, and autonomy interventions from global robot deployments
  • Collaborate with robotics, autonomy, and deployment teams to understand data requirements and priorities
  • Ensure data quality, reliability, and scalability across the data platform infrastructure
  • Maintain and optimize systems that process continuous streams of data from globally distributed sources

What they're looking for

  • Data pipeline design and implementation
  • ETL/ELT systems and architectures
  • Cloud data platforms (e.g., AWS, GCP, Azure)
  • Stream processing technologies
  • SQL and relational databases
  • Python or similar scripting languages
  • Data integration and API design
  • Distributed systems and scalability principles
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Field AI

Field AI develops embodied AI and autonomous robotics systems for real-world deployment in industrial environments like oil & gas and mining. The company is hiring software engineers to build web-based systems, perception and validation pipelines, test infrastructure, ROS-based robotic software, and customer-facing products that integrate AI with field-deployed hardware.

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Likely interview questions

  • Describe your experience designing data pipelines that handle high-volume, continuous data streams from distributed sources.
  • How would you approach ensuring data reliability and quality when ingesting telemetry from thousands of robots in varied environments?
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