Field Robotics Automation Engineer

Field Ai; Irvine (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

FieldAI’s Irvine team builds risk-aware, field-ready autonomous systems and deploys them on real robots. As an Automation Engineer, you will work with our deployed robot fleet to improve reliability and performance across real-world sites at the intersection of robotics, machine learning, and field operations.

You’ll own triage, data labeling, model evaluation, and hands-on ML tasks including building datasets and fine-tuning models for site-specific use cases.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Robotics, AI, or related field.
• Strong Python and PyTorch experience.
• Experience training and evaluating ML models.
• Experience with C++ and production ML/robotics systems.
• Ability to work across ML and robotics teams.

Responsibilities

• Own day-to-day triage of issues across deployed sites and identify the highest‑impact failure modes.
• Analyze robot behavior, logs, telemetry, and model outputs to diagnose issues across perception, prediction, and planning.
• Track failure modes and drive issues through resolution, with a focus on consistently improving site performance.
• Build datasets and evaluation sets for new failure modes and site‑specific use cases.
• Train, fine‑tune, and evaluate ML models as required for new failure modes or site‑specific challenges.
• Collaborate with deployment and robotics engineers to validate improvements on robots.

Skills

Python

PyTorch

C++

ML deployment

Debugging

Robotics systems

Data pipelines

Cross-team work

Education

BS/MS in CS/Robotics

Tools

ROS

Linux

Git

Jupyter

Job description

FieldAI’s Irvine team builds risk-aware, field-ready autonomous systems and deploys them on real robots. As an Automation Engineer, you will work with our deployed robot fleet to improve reliability and performance across real-world sites at the intersection of robotics, machine learning, and field operations.

You’ll own triage, data labeling, model evaluation, and hands-on ML tasks including building datasets and fine-tuning models for site-specific use cases.