Applied AI Engineer, Learning Intelligence

About the role

As an Applied AI Engineer, you will be responsible for designing and maintaining a skill and concept graph that maps relationships between various learning elements. Your work will involve developing machine learning models to infer learner skill levels based on usage patterns and other data, moving beyond self-reported inputs. A key part of this role is building and iterating on recommendation systems that suggest learning paths and generate dynamic content.

You will collaborate with frontend engineers to ensure that AI outputs are correctly integrated and presented with appropriate context to users. Defining explainability standards for model outputs is also crucial, enabling users and stakeholders to understand the rationale behind recommendations. This position requires close partnership with product and content teams to validate recommendation quality and establish feedback loops.

Success in this role means consistently monitoring model performance in production and owning the evaluation framework for recommendation quality. You will be instrumental in delivering AI-driven features that significantly enhance the learner experience by providing personalized and effective learning content and pathways.

The base salary for this role ranges from $111,200 to $191,050 USD, with the specific amount depending on factors like skills, experience, and location zone.

Skills that matter here

• machine learning: You will develop ML models to infer learner skill levels and build recommendation systems.
• knowledge representation: This role requires designing and maintaining a skill and concept graph that maps relationships between learning content.
• product engineering: You will partner with frontend engineers to integrate AI outputs and ensure features are delivered effectively to users.
• Python: Advanced Python proficiency is required for architecting robust, production-grade applications.
• LLM APIs: You will use LLM APIs and prompt engineering to develop generative features and ship LLM-based systems to production.
• graph databases: Hands-on experience with graph databases is necessary for building and managing knowledge graphs.

Who this role suits

• You have a background in applied ML or data science, with a focus on production recommendation or personalization systems.
• You possess a high degree of intellectual curiosity and are adept at finding elegant, straightforward solutions to complex problems.
• You are an exceptional communicator, capable of translating technical logic for diverse stakeholders.
• You have a hands-on history of shipping LLM-based systems to production, including large-scale deployment and evaluation frameworks.

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.