Instructure

1 month ago

Senior Applied Data Scientist (Retrieval and Semantic Systems)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Own end-to-end retrieval and semantic systems that serve as a core, reusable capability across multiple AI products. Design and ship production retrieval pipelines, including vector store selection/operation, indexing and refresh workflows, semantic and hybrid retrieval, and reranking. Build retrieval evaluation with gold sets and offline relevance metrics, drift detection, and regression gates. Partner with product, research, and AI Platform/infrastructure teams to deliver measurable relevance at scale.

Location: Budapest

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Design, build, and ship production retrieval systems powering AI capabilities across multiple products.
•Own vector store selection and operation, covering scalability, latency, reliability, cost, and multi-tenant design.
•Build and operate indexing and refresh pipelines, including chunking, embedding generation, backfills, deletes, and versioned indices.
•Implement semantic and hybrid retrieval with embeddings, similarity search, lexical/vector combination, metadata filtering, and reranking.
•Own retrieval evaluation with gold sets, offline relevance metrics, slice analysis, drift detection, and regression gates.

Pay and Benefits

Perks:Paid LeaveLearning Budget

Key Requirements

•6+ years of experience building and shipping production machine learning or applied AI systems.
•Proven ownership of a retrieval system in production, including vector store selection and operation.
•Strong Python skills and experience building services and APIs (e.g., FastAPI).
•Solid grounding in embeddings, approximate nearest neighbor search, and retrieval and ranking systems.
•Demonstrated ability to define and run retrieval evaluation using gold sets, relevance metrics, slice analysis, and regression prevention.

Experience:6+ yearsMachine learning

Skills:OwnershipTradeoff judgmentPartnering

Tech Stack:PythonFastAPIEmbeddingsVector storeApproximate nearest neighbor searchRerankingLLMAWS

Company Brief

Instructure

Builds and delivers learning management and education technology solutions, best known for the Canvas LMS platform, serving K–12 schools, higher education institutions, and corporate learning programs worldwide.

Industry: EdTech

Company Size: Enterprise (1,001+ employees)

Growth: Established Company

Funding: Private Equity Backed

Headquarters: Salt Lake City, United States

Founded: 2008

Website LinkedIn

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Instructure

1 month ago

Senior Applied Data Scientist (Retrieval and Semantic Systems)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Location: Budapest

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Design, build, and ship production retrieval systems powering AI capabilities across multiple products.
•Own vector store selection and operation, covering scalability, latency, reliability, cost, and multi-tenant design.
•Build and operate indexing and refresh pipelines, including chunking, embedding generation, backfills, deletes, and versioned indices.
•Implement semantic and hybrid retrieval with embeddings, similarity search, lexical/vector combination, metadata filtering, and reranking.
•Own retrieval evaluation with gold sets, offline relevance metrics, slice analysis, drift detection, and regression gates.

Pay and Benefits

Perks:Paid LeaveLearning Budget

Key Requirements

•6+ years of experience building and shipping production machine learning or applied AI systems.
•Proven ownership of a retrieval system in production, including vector store selection and operation.
•Strong Python skills and experience building services and APIs (e.g., FastAPI).
•Solid grounding in embeddings, approximate nearest neighbor search, and retrieval and ranking systems.
•Demonstrated ability to define and run retrieval evaluation using gold sets, relevance metrics, slice analysis, and regression prevention.

Experience:6+ yearsMachine learning

Skills:OwnershipTradeoff judgmentPartnering

Tech Stack:PythonFastAPIEmbeddingsVector storeApproximate nearest neighbor searchRerankingLLMAWS

Company Brief

Instructure

Industry: EdTech

Company Size: Enterprise (1,001+ employees)

Growth: Established Company

Funding: Private Equity Backed

Headquarters: Salt Lake City, United States

Founded: 2008

Website LinkedIn