Instructure

1 month ago

Senior Data Scientist (AI Platform)

✓ 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 the end-to-end machine learning lifecycle for AI platform capabilities, taking models from prototype to reliable, production-grade systems. Design and operate inference services, APIs, and deployment/orchestration pipelines with strong MLOps practices—versioning, testing, monitoring, rollback, and evaluation for quality, latency, and cost. Build reusable platform patterns and uphold production ML engineering standards while partnering with product, engineering, research, and infrastructure teams.

Location: Budapest

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Architect, build, and operate scalable inference services, APIs, and backend components for model-driven and LLM-powered product features.
•Productionize AI/ML workflows using MLOps practices such as model versioning, testing, deployment pipelines, monitoring, rollback, and operational reliability.
•Define and implement evaluation frameworks for model quality, system reliability, latency, and cost so models ship with consistent standards.
•Build reusable platform patterns, service templates, and reference implementations that other teams can adopt.
•Set and uphold AI engineering standards (code quality, documentation, observability, incident readiness) and mentor team members in production ML practices.

Pay and Benefits

Perks:Paid LeaveHealth InsuranceLearning Budget

Key Requirements

•6+ years of experience in software engineering, machine learning engineering, or applied AI engineering with clear ownership of production systems.
•Demonstrated experience taking ML and LLM systems from prototype to production and operating them in live environments.
•Strong experience building and operating APIs and services (Python preferred), working with containers, and debugging production reliability and performance issues.
•Strong MLOps skills including deployment and orchestration pipelines, model/artifact versioning, monitoring, and rollback for ML/LLM workloads.
•Working knowledge of modern AI patterns including embeddings, retrieval, semantic search, and RAG with production constraints.

Skills:Operational reliabilityCollaborationMentorshipEngineering standards

Tech Stack:PythonAPIsInference servicesContainersKubernetesCIModel versioningMonitoringRollbackEmbeddingsRetrievalSemantic searchRAGLLM

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

Loading job details...

Preparing the role view and application actions.

Instructure

1 month ago

Senior Data Scientist (AI Platform)

✓ 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

•Architect, build, and operate scalable inference services, APIs, and backend components for model-driven and LLM-powered product features.
•Productionize AI/ML workflows using MLOps practices such as model versioning, testing, deployment pipelines, monitoring, rollback, and operational reliability.
•Define and implement evaluation frameworks for model quality, system reliability, latency, and cost so models ship with consistent standards.
•Build reusable platform patterns, service templates, and reference implementations that other teams can adopt.
•Set and uphold AI engineering standards (code quality, documentation, observability, incident readiness) and mentor team members in production ML practices.

Pay and Benefits

Perks:Paid LeaveHealth InsuranceLearning Budget

Key Requirements

•6+ years of experience in software engineering, machine learning engineering, or applied AI engineering with clear ownership of production systems.
•Demonstrated experience taking ML and LLM systems from prototype to production and operating them in live environments.
•Strong experience building and operating APIs and services (Python preferred), working with containers, and debugging production reliability and performance issues.
•Strong MLOps skills including deployment and orchestration pipelines, model/artifact versioning, monitoring, and rollback for ML/LLM workloads.
•Working knowledge of modern AI patterns including embeddings, retrieval, semantic search, and RAG with production constraints.

Skills:Operational reliabilityCollaborationMentorshipEngineering standards

Tech Stack:PythonAPIsInference servicesContainersKubernetesCIModel versioningMonitoringRollbackEmbeddingsRetrievalSemantic searchRAGLLM

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