Senior Infrastructure Engineer, AI/ML Systems

At Instructure , we believe in the power of people to grow and succeed throughout their lives. Our goal is to amplify that power by creating intuitive products that simplify learning and personal development, facilitate meaningful relationships, and inspire people to go further in their education and careers. We do this by giving smart, creative, passionate people opportunities to create awesome. And that's where you come in:

Our AI team is where a lot of that gets built: applying advanced AI to real problems in learning, and turning research into product capabilities that educators and students use every day.

This is a hands-on delivery role. You'll build the deployment path that takes models and AI services from prototype to production, and you'll keep them running once they're there. You'll be one of two engineers who own infrastructure for this team, which means wide scope, real ownership, and direct influence over how we build.

You'll work closely with data scientists, applied AI engineers, and product partners to turn advanced AI ideas into reliable product capabilities used at scale.

What You'll Do

Build and operate deployment pipelines for AI services, covering CI/CD, infrastructure-as-code, environment promotion, and rollback

Deploy and operate model serving, batch scoring, and orchestration pipelines across development, staging, and production

Partner with data scientists and applied AI engineers to take prototypes into production, including system design for net-new services

Own production reliability for AI services: monitoring, alerting, debugging, performance, and cost

Spot repeated patterns and turn them into reusable templates, so the team can ship its second and third variant of something without rebuilding it

What You'll Need

Six or more years in infrastructure, DevOps, platform, or ML engineering, with ownership of systems running in production

Deep hands-on experience across a wide range of AWS services, including compute, networking, storage, deployment, and monitoring

Infrastructure-as-code experience (Terraform, CDK, or CloudFormation)

Experience with containers and modern deployment patterns (Docker required, Kubernetes or ECS/EKS a plus), applied to CI/CD pipelines you've designed and operated for production services

Experience with orchestration and workflow tooling (Airflow, Dagster, Argo, Step Functions, or similar)

Comfort working through ambiguity and collaborating directly with data scientists and researchers

It Would Be a Bonus If You Had

Experience with ML platform components and data pipeline orchestration at scale

Experience running LLM-based or retrieval-based systems in production

Experience operating specialized data stores, including graph databases

Experience building internal tooling, templates, or reference implementations that other engineers adopted

Onsite Collaboration Requirement: This role requires working onsite on Tuesday and Wednesday, with Thursday strongly encouraged as part of our company’s in-person collaboration model.

Why Join Us