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Field AI

Project Engineer _Field Application Engineering

Irvine, CAfull timemidAdded 1 month ago

About this role

Field AI seeks a Project Engineer to manage end-to-end field deployments of autonomous robotic systems across construction, security, and manufacturing sites. The role combines project management, logistics coordination, and on-site technical support to ensure successful real-world robot deployments.

What you'll do

  • Plan and coordinate field deployments of robotic systems from pre-deployment through execution and post-deployment support
  • Manage logistics, scheduling, and resource allocation across engineering, operations, and customer teams
  • Serve as primary on-site technical contact during deployments in often challenging field environments
  • Communicate project status and requirements across internal teams and external customers
  • Ensure deployments meet quality standards, timelines, and customer expectations
  • Document deployment processes and gather insights for continuous improvement

What they're looking for

  • Project management and coordination
  • Field operations and logistics planning
  • Technical troubleshooting and systems knowledge
  • Stakeholder communication and relationship management
  • Organizational and attention to detail
  • Robotics or hardware systems experience
  • Ability to work in fast-paced, dynamic environments
  • Problem-solving and adaptability
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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.

View all jobs at Field AI

Likely interview questions

  • Can you walk us through a complex field deployment or technical project you've coordinated from start to finish? What were the biggest challenges and how did you overcome them?
  • How have you handled situations where field conditions differed significantly from lab testing or initial planning assumptions?
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