Data Platform Engineer, Infrastructure

About the 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.

About the Job As a Data Platform Engineer, Infrastructure, you will build and operate the cloud foundation the entire data platform runs on — the systems every robot log, dashboard, and model-training run depends on. You will collaborate with pipeline and analytics engineers, as well as edge and robotics teams, to keep the platform reliable, secure, and cost-efficient as our fleet grows.

About the Data Platform Team Every robot we deploy generates a continuous stream of telemetry, sensor logs, and operational data from environments around the world. The Data Platform team builds the systems that capture every autonomy intervention, anomaly, and operational signal from globally deployed robots, classify them, and turn them into the ranked problem list that drives our engineering roadmap.

What You'll Get To Do - Design, build, and operate the cloud infrastructure for the data platform: compute (Kubernetes, serverless, batch clusters), storage (object stores, warehouse/lakehouse), and networking.

- Manage everything as code: Terraform/IaC, GitOps workflows, and CI/CD for infrastructure and data services.

- Provision and tune infrastructure for heavy workloads: large-scale sensor data processing, distributed pipeline execution, and ML training and evaluation jobs.

- Own platform reliability: SLAs/SLOs, on-call practices, incident response, and capacity planning as fleet data volume grows.

- Implement security and governance: IAM and access controls, secrets management, encryption, audit logging, and environment isolation.

- Own cloud cost management: monitoring, budgeting, and optimization across storage and compute.

- Build the observability stack (metrics, logging, tracing) for all data platform services.

- Build internal tooling and paved paths so pipeline and analytics engineers can ship without becoming infrastructure experts.

What You Have - Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.

- 3–5+ years of experience in infrastructure, platform, DevOps, or SRE roles, ideally supporting data-intensive systems.

- Strong experience with a major cloud (AWS or GCP), Kubernetes, and infrastructure-as-code (Terraform or similar).

- Strong programming skills in Python, Go, or similar for automation and internal tooling.