Senior AI/ML Platform Engineer

Senior AI/ML Platform Engineer 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 team builds AI-native capabilities, reusable AI systems, and shared infrastructure that power multiple products and workflows across the platform. We are looking for a Senior AI/ML Platform Engineer to build the foundations that make AI capabilities shippable and reliable at scale. This role owns the platform patterns, deployment workflows, and operational standards that enable teams to move from prototype to production safely and repeatedly. You will work closely with product, engineering, and research partners to turn advanced AI ideas into reliable product capabilities used at scale. What You’ll Do - Build and improve CI/CD workflows for ML/AI services across development, staging, and production environments - Establish reusable service templates and “golden path” patterns for AI microservices (logging, metrics, tracing, config, secrets, versioning) - Design and operate orchestration patterns for batch and online workloads on AWS - Define operational standards for AI services: monitoring, alerting, SLOs, runbooks, incident readiness, and rollback strategies - Partner with applied AI and research teams to productionize prototypes into stable services without slowing iteration velocity Influence platform architecture and engineering standards across a growing Applied AI team What You’ll Need - 7+ years of experience in software engineering, platform engineering, ML engineering, or a closely related role with production ownership - Demonstrated experience designing and operating CI/CD pipelines and release processes for production services - Strong AWS experience (compute, storage, networking, deployment, monitoring) - Experience with containers and modern deployment patterns (Docker required; Kubernetes or equivalent is a plus) - Experience with orchestration and workflow tooling (for example, Airflow, Dagster, Argo, Step Functions, or similar) - Strong operational mindset and experience supporting production systems (debugging, reliability, performance, cost) - Ability to set pragmatic standards, work through ambiguity, and collaborate across engineering, product, and research teams It Would Be a Bonus If You Had - Experience building ML platform components (feature stores, model registries, training pipelines, model serving patterns) - Experience with infrastructure-as-code and environment automation - Experience building evaluation and observability systems for AI services (quality metrics, drift signals, regression gates) - Experience supporting retrieval or LLM-enabled systems in production - Experience mentoring engineers or leading technical standards in a platform-building environment Growth & Impact - In This Role, You’ll Be Expected To In this role, you will define how AI services are built and operated across a growing portfolio of products. You will influence standards that directly improve prototype-to-production speed, system reliability, and the ability to ship multiple AI capabilities in parallel. 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. Our projects have included making sense of unstructured feedback, applying large language models to save teachers’ time and improve student experiences, classifying partner networks fo ... (truncated, view full listing at source)