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:
Instructure is building foundational AI and machine learning capabilities that will power the next generation of learning experiences across our product ecosystem. We are looking for a Senior AI/ML Platform Architect / Engineer to design and build the AWS-native infrastructure layer that enables data scientists, ML engineers, and applied AI teams to move from prototype to production safely, reliably, and at scale.
This is not a research role and not a traditional DevOps role. It is a hands-on systems architecture role for someone who understands cloud infrastructure, production reliability, and the unique needs of AI/ML workloads.
You will partner closely with data science, applied AI, backend engineering, platform engineering, and product teams to translate emerging AI/ML needs into scalable, modular, production-grade systems.
Design and build the AWS-native production infrastructure for AI/ML services, including deployment, observability, reliability, and operational readiness.
Partner with ML, data science, and applied AI teams to understand their workflows and translate prototype needs into scalable architecture.
Create reusable infrastructure patterns, service templates, CI/CD pipelines, and deployment workflows for AI/ML workloads.
Architect systems for model serving, batch inference, retrieval pipelines, evaluation workflows, and AI service deployment.
Define production standards for monitoring, alerting, rollback, logging, versioning, and reliability of AI/ML systems.
Collaborate with platform and backend engineering teams to ensure AI/ML infrastructure aligns with broader company architecture and security standards.
Strong experience designing and operating production systems on AWS.
Deep understanding of distributed systems, cloud architecture, scalability, reliability, and service design.
Hands-on experience with infrastructure-as-code, CI/CD, Docker, Kubernetes, and production deployment workflows.
Experience building or supporting production ML, AI, data, or high-scale backend systems.
Strong system design skills, including the ability to reason about tradeoffs, failure modes, data flow, service boundaries, and operational complexity.
Ability to communicate clearly across data science, ML engineering, backend engineering, platform engineering, product, and leadership stakeholders.
Experience with SageMaker, Bedrock, ECS, EKS, Lambda, S3, RDS, OpenSearch, Aurora, EventBridge, Step Functions, or related AWS services.
Experience with model serving, batch inference, embedding pipelines, vector databases, RAG systems, or LLM-backed applications.
Experience building ML platform capabilities such as model registries, experiment tracking, evaluation pipelines, inference services, or model monitoring.
Experience supporting both real-time and batch AI/ML workloads.
Experience with workflow orchestration, data pipelines, and production evaluation frameworks.
Experience defining production-readiness standards for AI systems, including evaluation gates, model/version drift, data quality checks, and cost monitoring.
You enjoy designing systems from first principles and can explain architecture tradeoffs clearly.
You have built infrastructure that other engineers depend on.
You are comfortable operating at both architecture and implementation levels.
You understand that ML systems involve data, models, evaluation, versioning, latency, uncertainty, and operational risk.
You can take an ambiguous AI/ML need and turn it into a practical technical architecture.
You care deeply about scale, reliability, modularity, maintainability, and developer experience.
You understand the AI/ML team’s workflows, infrastructure gaps, and production bottlenecks.
You define reusable architecture patterns that help AI/ML services move from prototype to production.
You establish reliable deployment, monitoring, rollback, and operational standards for AI/ML systems.
You reduce friction for data scientists and applied AI engineers by creating clear production pathways.
You help teams build AI/ML systems that are scalable, secure, observable, and maintainable.
Your work becomes part of the foundation for scaling AI capabilities across Instructure products.
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.
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.