Overview
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 ris
Full job description
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.
Build and maintain CI/CD pipelines across robotics, AI, and software systems using tools such as GitHub Actions.
Design and manage cloud-native, containerized infrastructure using Kubernetes, Docker, and related technologies.
Deploy and support workloads across cloud and edge environments.
Improve developer workflows through automation, tooling, and infrastructure enhancements.
Support testing, packaging, deployment, and observability for distributed robotics and AI systems.
Collaborate cross-functionally with robotics, ML, and platform engineering teams to improve reliability and scalability.
Optimize build systems, monorepos, and deployment workflows for complex engineering environments.
Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent practical experience.
3-7 years of experience in DevOps, Platform Engineering, Cloud Infrastructure, SRE, or related roles.
Strong experience with Kubernetes, Docker, and cloud-native infrastructure.
Experience building and maintaining CI/CD systems in complex engineering environments.
Strong scripting or programming skills in Python, Bash, Go, or similar languages.
Solid understanding of Linux systems, networking, and distributed systems fundamentals.
Strong problem-solving skills and ability to work effectively across multidisciplinary teams.
Experience supporting robotics, autonomous systems, IoT, or edge computing environments.
Experience with MLOps, AI infrastructure, GPU workloads, or ML deployment pipelines.
Familiarity with ROS, Terraform, Helm, ArgoCD, or related infrastructure tooling.
Experience managing large monorepos, build systems, and developer platforms.
Experience building observability and reliability systems for distributed infrastructure.
Kubernetes certifications such as CKA, CKAD, or CKS.
Bias for action and ability to thrive in fast-moving, ambiguous environments.
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