About the role
Netic is the AI revenue engine for essential services who are the backbone of the American economy. With $43M in funding from Founders Fund, Greylock, Hanabi, and Dylan Field who led our Series B, we helped our customers book hundreds of thousands of jobs across services industries in North America. There are now companies operating entirely AI-first on Netic. You’ll join our team with relentless builders from Scale, Databricks, HRT, Meta, MIT, Stanford, and Harvard in bringing frontier AI to the physical economy, where the problems are hard, the data is complex, and the impact is immediate and tangible. As a founding Product Infrastructure Engineer , you’ll architect and scale the backbone supporting our autonomous AI agents—tackling real-world challenges with immediate, tangible impact. You’ll collaborate with a driven team of builders to shape infrastructure and processes from the ground up, leveraging cutting-edge cloud and orchestration technologies. If you thrive in fast-paced, ambiguous environments and are excited to set new standards in the agentic space, this is your opportunity to build and leave your mark.
What You'll Do Build cloud infrastructure: Design and operate the backbone that hosts our AI agents and supports our platform. Automate operations: Create infrastructure as code and automated deployment pipelines for reliable releases. Enable scale: Implement systems that handle usage spikes gracefully through autoscaling and multi-region support. Create observability: Build monitoring, logging, and dashboards that provide real-time visibility into system health. Maintain security: Implement security best practices including IAM, network segmentation, and audit trails. What You'll Bring Infrastructure experience: 4+ years running distributed systems at scale with a major cloud platforms (we use GCP but AWS and Azure is great, too). Automation skills: Proven record of owning infrastructure-as-code and CI/CD pipelines (Terraform, Git Actions, etc). Performance expertise: Experience optimizing systems and databases to meet latency and cost targets under multi-modal workloads. For example, experience with pgBouncer, Kubernetes-based autoscaling, and similar tools. Observability knowledge: Fluent with modern monitoring and tracing tooling (we use Datadog) and built-in tools in Vercel or GCP. Security awareness: Understanding of enterprise security