Instructure, Inc. · Budapest, Hungary, HU · 10 days ago
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.
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 Data Scientist to own retrieval and semantic systems end to end, as a core, reusable capability that multiple AI products depend on. You will own the full retrieval vertical: vector store selection and operation, indexing and refresh pipelines, semantic and hybrid retrieval, reranking, and the evaluation systems that prove relevance is good and stays good. You will own retrieval-specific architecture and its day-to-day operation, while our infrastructure owner provides the underlying cloud, cluster, and CI substrate and our AI Platform engineers provide the general MLOps and service scaffolding you build on.
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 a deep individual-contributor specialist role. We are looking for someone who has owned a retrieval system in production, not someone who has only used a vector database in a prototype. Retrieval evaluation is central to this role: if you cannot measure relevance and catch regressions before they reach users, the system is not done.
In this role, you will own retrieval and semantic search as a core differentiator that many AI products build on. You will set how retrieval is architected, operated, and evaluated at Instructure, and you will be the person accountable for relevance being good, measurable, and durable as the system and its content evolve.
Join us and help shape the future of education by turning cutting-edge AI into reliable product capabilities.
At Instructure, we're on a mission to help educators and students learn together, anytime, anywhere, and however works best. You'll join our research-driven team tackling education's biggest challenges with cutting-edge technology. Our projects have included making sense of unstructured feedback, applying large language models to save teachers' time and improve student experiences, classifying partner networks for smarter recommendations, and detecting fraud to protect resources for real learners.
We value diversity, creativity, and passion, and invest in our teams through mentorship, hack weeks, internal conferences, and a culture where innovation thrives. Here, you'll have the chance to build the next generation of LMS features that make a real impact on students and teachers, and do it in a collaborative, supportive environment that encourages experimentation and growth.
Get in on all the awesome at Instructure!
We offer competitive, meaningful benefits in every country where we operate. While they vary by location, here's a general idea of what you can expect:
We believe in hiring great people and treating them right. The more diverse we are, the better our ideas and outcomes.
Instructure is an Equal Opportunity Employer. We comply with applicable employment and anti-discrimination laws in every country where we operate.
All employees must pass a background check as part of the hiring process. To help protect our teams and systems, we’ve implemented identity verification measures. Candidates may be asked to verify their legal name, current physical location, and provide a valid contact number and residential address, in accordance with local data privacy laws.
Any attempt to misrepresent personal or professional information will result in disqualification.
Headquarters
Budapest, Hungary
Work Location
hybrid
Job Category
Data Science / AI / Machine Learning
Application Deadline
Not specified
Job Type
Full Time
Experience Level
senior-level
Application Method
Apply via Website
Salary
Not specified
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