Senior Infrastructure Engineer, AI/ML Systems
Summary
The Senior Infrastructure Engineer will build and maintain the deployment paths for AI models and services, moving them from prototype to production within an educational technology environment. The role involves working closely with data scientists and researchers to manage infrastructure using AWS, containerization, and infrastructure-as-code tools.
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.
Why Join Us
Join us and help shape the future of education by turning cutting-edge AI into reliable product capabilities.
At Instructure, we're on a mission to help educators and students learn together, anytime, anywhere, and however works best. You'll join our research-driven team tackling education's biggest challenges with cutting-edge technology.
We value diversity, creativity, and passion, and invest in our teams through mentorship, hack weeks, internal conferences, and a culture where innovation thrives. Here, you'll have the chance to build the next generation of LMS features that make a real impact on students and teachers, and do it in a collaborative, supportive environment that encourages experimentation and growth.
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.