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 DoBuild 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
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
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.
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.
Get in on all the awesome at Instructure!
We offer competitive, meaningful benefits in every country where we operate. While they vary by location, here's a general idea of what you can expect:
Competitive compensation, plus all full-time employees participate in our ownership program - because everyone should have a stake in our success.
Flexible work culture. Our remote, hybrid and in-office collaboration spaces vary by role, team and location.
Generous time off, including local holidays and our annual “Dim the Lights” period in late December, when teams are encouraged to step back and recharge based on departmental needs.
Comprehensive wellness programs and mental health support
Learning and development resources, including professional development tools and tuition reimbursement, to support your growth
The technology and tools you need to do your best work
Motivosity employee recognition program
A culture rooted in inclusivity, support, and meaningful connection
We believe in hiring great people and treating them right. The more diverse we are, the better our ideas and outcomes.
Instructure is an Equal Opportunity Employer. We comply with applicable employment and anti-discrimination laws in every country where we operate.
All employees must pass a background check as part of the hiring process. To help protect our teams and systems, we’ve implemented identity verification measures. Candidates may be asked to verify their legal name, current physical location, and provide a valid contact number and residential address, in accordance with local data privacy laws.
Any attempt to misrepresent personal or professional information will result in disqualification.
Skills Required
- Six or more years of experience in infrastructure, DevOps, platform, or ML engineering, including ownership of production systems
- Deep hands-on experience with AWS services across compute, networking, storage, deployment, and monitoring
- Infrastructure-as-code experience with Terraform, AWS CDK, or CloudFormation
- Production experience with Docker and modern deployment patterns applied to CI/CD pipelines
- Experience with Kubernetes or ECS/EKS
- Experience with orchestration and workflow tools such as Airflow, Dagster, Argo, Step Functions, or similar
- Ability to work through ambiguity and collaborate directly with data scientists and researchers
- Experience with ML platform components and large-scale data pipeline orchestration
- Experience operating LLM-based or retrieval-based systems in production
- Experience operating specialized data stores, including graph databases
- Experience building internal tooling, templates, or reference implementations adopted by other engineers
Instructure Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Instructure and has not been reviewed or approved by Instructure.
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Fair & Transparent Compensation — Pay is considered market-competitive for many engineering, product, and quota-carrying roles, especially when factoring base, variable, and equity. In these tracks, total rewards are often characterized as fair-to-strong for the role and location.
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Healthcare Strength — Health coverage is described as comprehensive, including medical, dental, vision, mental-health support, and HSA/FSA options. Employer contributions are often portrayed as strong, and core medical benefits receive consistently positive marks.
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Leave & Time Off Breadth — Time off offerings include flexible or “unlimited” PTO, paid holidays, and paid sick time, paired with widespread remote/hybrid flexibility. This breadth of time off and work flexibility is often viewed as a meaningful perk that enhances overall value.
Instructure Insights
What We Do
Instructure is helping people grow from the first day of school to the last day of work. More than 30 million people use its Canvas and Bridge platforms for learning management and employee development.







