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Field AI

Data Platform Engineer, Autonomy Analytics

Irvine, CAfull timemidAdded today

About this role

Field AI seeks a Data Platform Engineer to design and build data systems that reliably move 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, autonomy, ML training, and operations for a robotics company.

What you'll do

  • Design and build data pipelines that reliably ingest telemetry, sensor logs, and operational data from field robots
  • Integrate data systems across robotics, autonomy, and deployment operations teams
  • Classify and process autonomy interventions, anomalies, and operational signals from global robot deployments
  • Support analytics, ML training, and deployment operations with reliable data infrastructure
  • Collaborate with cross-functional teams to understand data requirements and deliver solutions
  • Optimize data systems for real-world environments and harsh operating conditions

What they're looking for

  • Data pipeline design and implementation
  • ETL/ELT systems and orchestration
  • Distributed systems and scalability
  • Data quality and reliability engineering
  • Cloud data platforms (e.g., AWS, GCP, Azure)
  • Telemetry and sensor data processing
  • SQL and data modeling
  • Collaboration with cross-functional teams
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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

  • Walk us through how you would design a data pipeline to reliably ingest telemetry from thousands of robots operating in harsh, remote environments with variable connectivity.
  • Describe your experience with data quality and reliability—how would you ensure data accuracy and availability for critical downstream consumers like ML training and operations?
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