Sr Data Scientist, AI & Analytics

Sr Data Scientist, AI & Analytics 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: ABOUT THE ROLE 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. WHAT YOU’LL DO - 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. WHAT WE’RE LOOKING FOR - 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. NICE TO HAVE - 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 MIGHT BE A GREAT FIT IF - 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, evalua ... (truncated, view full listing at source)