Staff Infrastructure Engineer

Standard AI is building a highly scalable and versatile platform-as-a-service architecture. We deploy a variety of algorithms to the cloud in a live-inference setting with high-throughput demands and must do so in a scalable, efficient way for all of our stores. This role is a unique opportunity to work at the cutting edge of building systems and tools that empower machine learning at scale. We're looking for someone passionate about infrastructure development and production engineering to help perfect and scale our system to hundreds of stores. You will design and build high-quality production systems that drive key improvements to our platform-as-a-service architecture. You’ll be working with a group of brilliant and highly skilled engineers. If this sounds like fun, we'd love to hear from you!

This is a full time role and can be based remotely anywhere within the US on an ongoing basis. Standard AI is a remote first company. We want our employees to have the flexibility to create work habits, locations, and schedules that best fit their lives.

What you'll do here:

• Design, develop, and maintain secure infrastructure for real-time and on-demand computer vision inference pipelines, backend services, and frontend apps in the cloud.
• Design, develop, and maintain infrastructure to provide developers with a safe and friction-less development, and build environments for on-prem deployments.
• Automate and optimize our infrastructure for security, scale, usability, and cost.
• Partner closely with other engineering teams to understand needs and help us deliver solutions to our customers.
• Mentor junior engineers and help establish best practices across the organization.
• Develop monitoring, set up alerting tools, and participate in a 24/7 on-call rotation for production systems.

What you’ve done before:

• Proficiency in programming, preferably with Go or Python.
• Proven experience building and managing secure, scalable cloud infrastructure as a service for internal users (GCP preferred, familiarity with AWS or Azure a plus).
• Deep understanding of containerization technologies (Docker, containerd) and container orchestration (Kubernetes) for efficient deployment and management of cloud services.
• Experience with CI/CD pipelines (Jenkins, GCP Cloud Build, Github Actions) and infrastructure as code tools (Terraform, Terragrunt) to automate deployments and configuration management.
• Strong knowledge of message queuing systems (Pub/Sub, SQS, SNS) to facilitate seamless development and delivery workflows.
• Familiarity with modern networking stacks and hardware installations is a plus.

Who we …