One Autonomy for All Robots. Field-proven embodied AI software that is finally unlocking the full potential of mobile robots in the real world. Backed by Khosla.
About the role
As an Automation Engineer at FieldAI , you will work closely with our deployed robot fleet to improve reliability and performance across real-world sites. You’ll sit at the intersection of robotics, machine learning, and field operations , owning the triage and resolution of issues across autonomy.
What they're looking for
- Bachelor’s or Master’s degree in Computer Science, Robotics, AI, or a related field
- Strong Python and experience with modern ML frameworks such as PyTorch
- Experience training, fine-tuning, and evaluating machine learning models
- Strong debugging and problem-solving skills, with the ability to work across ML and robotics systems
- Experience with C++ and production ML/robotics systems
- Understanding of ML data pipelines, labeling, evaluation, and model deployment
More about this role
FieldAI ’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.
As an Automation Engineer at FieldAI , you will work closely with our deployed robot fleet to improve reliability and performance across real-world sites. You’ll sit at the intersection of robotics, machine learning, and field operations , owning the triage and resolution of issues across autonomy.
A key part of the role is helping run our ML flywheel : identifying and prioritizing failure modes, curating and labeling data, working with modeling teams to improve models, and partnering with deployment...
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