Instructure Hungary Ltd
Budapest, Balatoni út 2/a
40% irodai munkavégzés, 60% távmunka
1.6M – 2.2M HUF alkalmazotti havi bruttó
Elvárások
- Machine learning
- applied ai engineering
- AI
- deployment
- CI/CD
- orchestration
- AWS
- Python
- RAG
- Data science
- applied data science
- software engineering
- Angol (C1)
A mi követelményeink
Eredeti szöveg. Fordítás megjelenítése
What You’ll Need
- 6+ years of experience in software engineering, machine learning engineering, applied AI engineering, or a closely related role with production ownership
- Demonstrated experience taking ML/AI systems from prototype to production in live environments
- Strong experience with deployment pipelines, CI/CD, orchestration, and operating production services on AWS
- Experience building and operating APIs/services (Python preferred), working with containers, and debugging reliability/performance issues
- Working knowledge of modern AI application patterns (for example, embeddings, retrieval, semantic search, or RAG) and the engineering constraints involved in running them in production
- Strong communication skills and the ability to work through ambiguity across engineering, product, and research teams
It Would Be a Bonus If You Had
- Experience building AI-native product features (not just internal analytics models)
- 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 (quality metrics, drift, failure analysis)
- Experience creating internal engineering standards, templates, or reference implementations adopted by multiple teams
- Experience in education technology, learning systems, or knowledge/skills modeling
- Experience mentoring engineers in a high-growth or platform-building environment
Pozíció / projekt rövid leírása
Eredeti szöveg. Fordítás megjelenítése
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 things.
Our Advanced Development 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 to help build, ship, and scale production AI systems from the ground up. This is an engineering-forward ML/AI systems role, intended for candidates who are comfortable moving from prototype to production and can own critical parts of the ML/AI systems lifecycle, including pipelines, model integration, inference services, deployment, monitoring, and operational reliability.
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 role is not primarily focused on BI/reporting or experimentation analytics. We are looking for someone with strong experience building and operating production ML/AI systems.
Growth & Impact – In This Role, You’ll Be Expected To
- Influence architecture and engineering standards for AI systems
- Shape reusable infrastructure and platform patterns
- Mentor a growing team of AI/ML engineers
- Turn advanced AI research into production value for educators and learners at scale
Napi feladatok
Eredeti szöveg. Fordítás megjelenítése
- Architect, build, and deploy production ML/AI systems that power customer-facing product capabilities
- Design and operate scalable inference services, APIs, and backend components for model-driven applications
- Build and improve data, feature, deployment, and orchestration pipelines on AWS across development, staging, and production environments
- Productionize AI workflows with strong MLOps practices, including CI/CD, versioning, testing, monitoring, rollback, and operational reliability
- Define and implement evaluation frameworks for model quality, system reliability, latency, and cost, and use those signals to improve production performance
- Partner with product, research, and engineering teams to turn prototypes into robust, scalable services, while driving strong engineering standards in code quality, documentation, observability, and incident readiness
Specifikációk
- Online állásinterjú
- Toborzás nyelvei: magyar&angol
- Azonnali kezdés
- Távmunka heti 3 nap
- Rugalmas munkaidő
A toborzási folyamat lépései
- recruiter screen
- technical interview 1
- technical interview 2
- manager round
Biztosított eszközök
- Apple
- Monitorok: Egy
Irodán belüli juttatások
- Ingyenes kávé
- Kerékpártároló
- Szórakozási zóna
- Zuhanyzó
- Ingyenes snack
- Ingyenes italok
- Ingyenes parkolás
- Belső képzések
- Belső hack napok
- Modern iroda
- Startup hangulat
- Nincs dress code
- Ingyenes reggeli
- Hack-weeks
- Employee assistance program
Extrák
- Nemzetközi projektek
- Kis létszámú csapat
- Lapos szervezet
