Skip to main content

Field AI

Software Engineer, Web

Irvine, CA$155k–$200kfull timemidAdded 1 month ago

About this role

FieldAI is seeking a Software Engineer for their Irvine robotics team to develop web-based systems supporting embodied AI and field-deployed robots. You'll work on risk-aware, reliable AI solutions that combine learning systems with rigorous engineering, with code tested on actual hardware and improved through real-world deployments.

What you'll do

  • Develop web interfaces and backend systems for robotics AI platforms
  • Build tools to support field deployment and real-time hardware testing
  • Contribute to risk-aware AI systems for embodied robotics applications
  • Collaborate with hardware and ML teams to integrate learning systems into production
  • Iterate on solutions based on real-world field deployment feedback
  • Engineer solutions that balance data-driven approaches with practical reliability

What they're looking for

  • Web development (frontend/backend)
  • Python or similar systems programming
  • API design and integration
  • Robotics systems familiarity
  • Cloud or distributed systems
  • Real-time system considerations
  • Version control and DevOps practices
  • Problem-solving with hardware constraints
Apply with Autofill

Opens the application — the Jobs AI extension fills it for you. Set up autofill

Opens the official application on the employer’s site. No login required.

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 describe your experience with robotics systems or embodied AI? What attracted you to working on field-deployed applications?
  • How have you approached debugging or optimizing performance when working with hardware sensors or real-world deployment constraints?