Applied AI Engineer, Learning Intelligence

About the role

This role focuses on enhancing learner experiences through AI-driven features. The engineer will design and maintain a comprehensive skill and concept graph, mapping relationships between skills, roles, domains, and learning content. This foundational work supports the development of intelligent systems that adapt to individual learner needs.

A core responsibility involves developing machine learning models to infer learner skill levels from diverse data sources, including usage patterns and assessments. The engineer will also build and refine recommendation systems that suggest learning modules, paths, and dynamically generate content, directly impacting how users engage with educational materials.

Success in this position means collaborating closely with product, content, and frontend engineering teams to ensure AI outputs are correctly integrated and understood. The engineer will define explainability standards for model recommendations, monitor performance in production, and establish evaluation frameworks to maintain high-quality recommendations.

The base salary for this remote role ranges from $111,200 to $191,050 USD, depending on factors such as experience, skills, and specific work location zone.

Skills that matter here

• machine learning: This role involves developing ML models to infer learner skill levels and building recommendation systems.
• knowledge representation: The engineer will design and maintain a skill and concept graph to represent relationships between learning elements.
• product engineering: The role requires collaborating with product teams to validate recommendation quality and integrate AI features into the product.
• Python: Advanced proficiency in Python is required for architecting robust, production-grade applications.
• LLM APIs: Experience with LLM APIs and prompt engineering is needed for developing generative features.
• graph databases: Hands-on experience with graph databases or ontology design is essential for building the skill and concept graph.

Who this role suits

• A professional with at least five years of experience in applied machine learning or data science.
• Someone with a proven track record of shipping LLM-based systems to production, including large-scale deployment.
• An individual who possesses intellectual curiosity and can develop elegant, straightforward solutions to complex problems.
• A clear communicator capable of explaining technical logic to both technical and non-technical stakeholders.

From the employer

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.

What We Are Looking For

• 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.

Benefits

• 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.
• 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.