Applied AI Engineer, Learning Intelligence

Databricks; United States; Remote; USD 130,000 - 190,000; Full time

Job summary

Databricks seeks an Applied AI Engineer to develop intelligent learning systems through machine learning and knowledge representation. The role focuses on designing a skill and concept graph that connects learning elements and developing ML models to infer learner skill levels from diverse data sources.

You will work with engineers to ensure AI outputs integrate smoothly into the user interface. In this role, you will help define standards for model explainability and validate the quality of

Qualifications

• Experience in developing ML-based learning systems.
• Knowledge of recommendation systems and graph databases.
• Familiarity with integrating AI into user interfaces.

Responsibilities

• Design and maintain a skill and concept graph for learning elements.
• Develop ML models to infer learner skill levels from diverse data sources.
• Collaborate with engineers to integrate AI outputs into the UI.
• Define standards for model explainability and validate recommendations.
• Monitor and improve model performance in production.

Skills

Recommendation systems

LLM APIs

Graph databases

Job description

Databricks seeks an Applied AI Engineer to develop intelligent learning systems through machine learning and knowledge representation. Candidates with experience in recommendation systems, LLM APIs, and graph databases should apply.

In this role, you will design and maintain a skill and concept graph that connects various learning elements, while developing ML models to accurately infer learner skill levels from diverse data sources. Your work will directly enhance user learning experiences by leveraging AI-driven recommendations.

You will collaborate with engineers to ensure seamless integration of AI outputs into the user interface, define standards for model explainability, and validate the quality of recommendations through feedback loops. Success in this position requires a proactive approach to monitoring and improving model performance in production.