Applied AI Engineer, Learning Intelligence

We are building the intelligence layer that powers how learners grow. This role sits at the intersection of machine learning, knowledge representation, and product engineering. You will own the skill and concept graph that defines what learners know and can do, infer skill gaps from behavioral and profile signals, and translate those inferences into personalized recommendations and dynamic learning that guide each learner to their next best step. You will also be the bridge between our AI capabilities and the engineers building our frontend, making sure AI-driven features ship in a way that is explainable, reliable, and production-ready.

What You Will Do

• Design, build, and maintain a skill and concept graph that maps relationships between skills, roles, domains, and learning content
• Develop ML models that infer learner skill levels from usage patterns, work output, assessments, and profile data (not just self-reported input)
• Build and iterate on recommendation systems that surface the next best module, suggest learning paths, and generate content dynamically
• Partner with frontend engineers to ensure AI outputs are consumed correctly, surfaced with appropriate context
• Define explainability standards for model outputs so users and stakeholders understand why a recommendation was made
• Collaborate with product and content teams to validate recommendation quality and close feedback loops
• Monitor model performance in production and own the evaluation framework for recommendation quality

• 5+ years of experience in applied ML or data science, with production recommendation or personalization systems in your background
• Hands-on experience with knowledge graphs, graph databases, or ontology design
• Experience with LLM APIs and prompt engineering for generative features
• Hands-on history of shipping LLM-based systems to production, including large-scale deployment, evaluation frameworks, and agentic workflows
• Advanced Python proficiency and experience architecting robust, production-grade applications
• Deep familiarity with the modern AI stack, from retrieval and agent frameworks to complex prompt engineering, model evaluation, and context engineering
• A high degree of intellectual curiosity and the ability to find elegant, straightforward solutions
• Exceptional communication skills, with the ability to translate technical logic for varied stakeholders

Pay Range Transparency

Design and maintain a skill and concept graph, build ML models to infer learner skills, and develop recommendation systems and LLM-driven features. Ship production-ready LLM systems, define explainability standards, partner with frontend and product teams, and monitor model performance and evaluation frameworks. The summary above was generated by AI, Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected base salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipated utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here. Zone 1 Pay Range $139,000-$191,050 USD Zone 2 Pay Range $125,000-$171,950 USD Zone 3 Pay Range $118,100-$162,350 USD Zone 4 Pay Range $111,200-$152,900 USD, At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.

Databricks is the data and AI company. More than 10,000 organizations worldwide - including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 - rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark , Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.