Senior Data Scientist, AI Platform

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 Data Scientist, AI Platform to own the machine learning lifecycle that turns models into reliable, production-grade product capabilities. You will design and operate the inference services, deployment and orchestration pipelines, and evaluation and monitoring frameworks that AI features depend on, and you will set the MLOps standards the rest of the team builds on. You will work alongside our infrastructure owner, who owns the underlying cloud, cluster, and CI substrate, while you own the ML systems that run on top of it. You will work closely with product, engineering, and research partners to turn advanced AI ideas into reliable product capabilities used at scale. Important note on scope: This is an MLOps and ML systems role, not a generic infrastructure or DevOps role, and not a BI/reporting or experimentation analytics role. We are looking for someone who has taken machine learning and LLM systems from prototype to production and operated them in live environments. Deep cloud, Kubernetes, and CI substrate expertise is valued but is owned by our infrastructure engineer; this role is accountable for the model lifecycle that runs on that substrate. WHAT YOU'LL DO - Architect, build, and operate scalable inference services, APIs, and backend components for model-driven and LLM-powered product features - Productionize AI and ML workflows with strong MLOps practices: model versioning, testing, deployment pipelines, monitoring, rollback, and operational reliability - Define and implement evaluation frameworks for model quality, system reliability, latency, and cost, and make these a standard part of how models ship - Build reusable platform patterns, service templates, and reference implementations that multiple teams and products can adopt - Set and uphold engineering standards across the AI team: code quality, documentation, observability, and incident readiness, and mentor team members in production ML practices - Partner with our infrastructure owner on the underlying cloud, cluster, and CI substrate, and with product, engineering, and research partners to move AI capabilities into production WHAT YOU'LL NEED - 6+ years of experience in software engineering, machine learning engineering, or applied AI engineering, with clear ownership of systems in production - Demonstrated experience taking ML and LLM systems from prototype to production and operating them in live environments. This is a hard requirement; we are not looking for a strong infrastructure engineer who has not worked with AI systems - Strong experience building and operating APIs and services (Python preferred), working with containers, and debugging reliability and performance issues in production - Strong MLOps skills: deployment and orchestration pipelines, model and artifact versioning, monitoring, and rollback for ML and LLM workloads - Working knowledge of modern AI patterns (embeddings, retrieval, semantic search, RAG) and their production constraints IT WOULD BE A BONUS IF YOU HAD - Experience with vector databases, retrieval infrastructure, or semantic indexing pipelines - Experience with graph databases or graph-based reasoning systems - Experience with observability and evaluation for LLM or retrieval systems, including quality metrics, drift, and failure analysis - A track record of internal engineering standards, templates, or reference implementations a