Instructure
Education Technology
SeniorAppliedAIEngineer,RetrievalandSemanticSystems
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Applied AI Engineer, Retrieval and Semantic Systems at Instructure. Skills: Applied AI, Retrieval systems, Semantic systems, Production ML. Design, build, and ship production retrieval systems. Own vector store selection and operation”
What You'll Achieve.
define retrieval as a core capability; establish standards and evaluation loops; make semantic systems reliable, measurable, and scalable
Industry & Context.
judgment on tradeoffs across relevance, latency, cost, and operational complexity
background check, identity verification measures, verify legal name, verify current physical location, provide valid contact number, provide residential address
What They're Looking For.
Must Have
6+ years of experience building and shipping production ML or applied AI systems, Proven experience owning a retrieval system in production, including vector store selection and operation, Python engineering skills and experience building services/APIs (for example, FastAPI or similar), Hands-on experience with embeddings, approximate nearest neighbor search concepts, and retrieval or ranking systems, Experience designing indexing and refresh strategies, including data quality controls and safe backfills, judgment on tradeoffs across relevance, latency, cost, and operational complexity, communication skills and ability to collaborate across engineering, product, and research teams
Nice to Have
Experience with hybrid retrieval (lexical plus vector), learning-to-rank, or domain-specific reranking, Experience with graph-structured context systems or knowledge graph integration, Experience building evaluation and observability for LLM or retrieval systems (quality drift, failure analysis, regression prevention), Experience with AWS-native architectures for retrieval and indexing services, Experience in education technology, content, curriculum, or skills modeling
What You'll Do.
and ship production retrieval systems
Own vector store selection and operation
Build indexing and refresh pipelines
Implement semantic retrieval patterns
Define and run retrieval evaluation
Partner with platform engineers on CI/CD
Own retrieval correctness and evolution
How You'll Work.
Team & Collaboration
Partner with platform engineers on deployment standards and observability; Work closely with product, engineering, and research partners; Collaborate across engineering, product, and research teams
Communication Scope
communication skills; ability to collaborate
Full Job Description
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 Applied AI Engineer to own retrieval and semantic systems end to end. This role builds and operates production retrieval as a core capability, including the retrieval infrastructure layer (indexing, storage, scaling, cost, and reliability), quality evaluation, and iteration loops that improve relevance over time. You will partner with platform engineers on deployment standards and observability, but you will own retrieval architecture decisions and day-to-day operation. 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 - Design, build, and ship production retrieval systems that power AI product capabilities - Own vector store selection and operation, including scalability, latency, reliability, cost, and multi-tenant design - Build indexing and refresh pipelines (chunking, embedding generation, backfills, deletes, versioned indices) - Implement semantic retrieval patterns, including embeddings, similarity search, metadata filtering, and reranking - Define and run retrieval evaluation: gold sets, offline metrics,, slice analysis, drift detection, and regression gates - Partner with platform engineers on CI/CD, service templates, monitoring, and incident readiness while owning retrieval correctness and evolution What You’ll Need - 6+ years of experience buil
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