Data Platform Engineer, Infrastructure

Location

Irvine, CA

Field AI is transforming how robots interact with the real world. We are building risk‑aware, reliable, and field‑ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data‑driven approaches or pure transformer‑based architectures, and are charting a new course. Already‑globally‑deployed solutions deliver real‑world results and rapidly improve models through real‑field applications.

Field AI is at the forefront of robotic embodied AI, transforming industries such as construction, security, mining, and manufacturing. Our autonomous robots operate globally, often in harsh environments, delivering critical insights to customers. Whether monitoring construction progress, ensuring safety compliance, or conducting predictive maintenance, Field AI is advancing technology to make a meaningful impact.

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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, classifies them, and turns them into the ranked problem list that drives our engineering roadmap.

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.

• 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, Git Ops 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.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
• 3–5+ years of experience in infrastructure, platform, Dev Ops, 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.
• Experience operating production systems: monitoring, alerting, incident response, and capacity planning.
• Working knowledge of data infrastructure components (warehouses, object storage, streaming systems, orchestrators) and what they demand from underlying infrastructure.
• Experience with security fundamentals: IAM, network security, secrets management.
• Strong problem‑solving skills and ability to work in interdisciplinary teams.

• Experience supporting data or ML platforms specifically (Spark/Databricks clusters, GPU scheduling, feature stores).
• Experience with high‑volume telemetry or IoT/edge fleets.
• Track record of significant cloud cost optimization.
• Prior experience as an early platform/infrastructure hire, building from zero.

Field AI believes in‑person collaboration is essential for tackling complex challenges. These are fully onsite roles based in our Irvine office, with…