CSQ227R13
Applied AI Engineer, Learning IntelligenceAbout the Role
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
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
Pay Range Transparency
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.
About Databricks
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.
Benefits
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.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
Skills Required
- 5+ years of experience in applied ML or data science with production recommendation or personalization systems
- Hands-on experience with knowledge graphs, graph databases, or ontology design
- Experience with LLM APIs and prompt engineering for generative features
- 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 modern AI stack: retrieval and agent frameworks, prompt engineering, model evaluation, context engineering
- Exceptional communication skills to translate technical logic for varied stakeholders
- High degree of intellectual curiosity and ability to find elegant solutions
Databricks Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Databricks and has not been reviewed or approved by Databricks.
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Equity Value & Accessibility — Equity grants and RSUs are a major part of total compensation and are highlighted for meaningful upside potential. Stock-based awards and refreshers contribute to strong overall pay positioning across senior technical and go-to-market roles.
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Healthcare Strength — Medical, dental, and vision coverage are complemented by mental-health resources, an EAP, and wellness reimbursements. Health benefits are consistently framed as comprehensive and competitive.
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Parental & Family Support — Paid parental leave for all parents, fertility support, and backup care options provide tangible assistance for family needs. Hybrid work norms and team-day structure further ease coordination for caregivers.
Databricks Insights
What We Do
As the leader in Unified Data Analytics, Databricks helps organizations make all their data ready for analytics, empower data science and data-driven decisions across the organization, and rapidly adopt machine learning to outpace the competition. By providing data teams with the ability to process massive amounts of data in the Cloud and power AI with that data, Databricks helps organizations innovate faster and tackle challenges like treating chronic disease through faster drug discovery, improving energy efficiency, and protecting financial markets.
