Databricks AI Engineer Interview Questions (2026)

Landing a AI Engineer role at Databricks requires targeted preparation. Databricks interviews include coding rounds, system design sessions, and domain-specific discussions around data platforms. Engineering candidates face questions about distributed computing, Apache Spark internals, and lakehouse architecture. The company values technical depth, open-source contributions, and the ability to simplify complex data challenges for users. This guide covers the most frequently asked questions and insider tips to help you succeed in your Databricks AI Engineer interview.

About the Databricks Interview Process

Databricks interviews assess deep expertise in data engineering, distributed systems, and passion for democratizing data and AI.

Databricks interviews include coding rounds, system design sessions, and domain-specific discussions around data platforms. Engineering candidates face questions about distributed computing, Apache Spark internals, and lakehouse architecture. The company values technical depth, open-source contributions, and the ability to simplify complex data challenges for users.

Why Databricks AI Engineer Interviews Are Different

Databricks AI Engineer interviews differ from standard AI Engineer interviews in several key ways. The company has a unique interview culture, specific evaluation criteria, and expects candidates to demonstrate alignment with their values and mission. Understanding these differences gives you a significant advantage over other candidates.

Top 10 AI Engineer Interview Questions at Databricks

1. At Databricks, you might be asked: Explain the transformer architecture and its significance.
2. A common Databricks interview question: How do you fine-tune large language models?
3. Databricks interviewers often ask: Describe your experience with computer vision models.
4. Databricks candidates should prepare for: How do you evaluate AI model fairness and bias?
5. A common Databricks interview question: What is RAG (Retrieval-Augmented Generation) and when would you use it?
6. Expect this at Databricks: How do you handle hallucinations in language models?
7. Databricks candidates should prepare for: Describe your experience with prompt engineering.
8. Expect this at Databricks: How do you deploy AI models at scale?
9. At Databricks, you might be asked: What is your approach to AI safety and responsible AI?
10. At Databricks, you might be asked: Describe a challenging AI project you worked on.

Databricks-Specific Preparation Tips for AI Engineer Candidates

• Study Apache Spark architecture, distributed computing, and lakehouse concepts
• Prepare for system design questions involving data pipelines and analytics platforms
• Practice coding problems with focus on data manipulation and distributed algorithms
• Research the lakehouse paradigm and how it unifies data warehousing and data lakes
• Show knowledge of MLOps, data governance, and modern data stack trends

General AI Engineer Interview Tips

• Stay current with the latest AI research papers
• Have hands-on experience with popular AI frameworks
• Understand both the theory and practical applications
• Be ready to discuss ethical implications of AI

Preparation Timeline for Databricks AI Engineer Interviews

• 4 weeks before: Research Databricks culture, recent news, and the specific team you are applying to.
• 2-3 weeks before: Practice technical questions daily and prepare behavioral stories using the STAR method.
• 1 week before: Do full mock interviews with HireFlow AI simulating Databricks interview style.
• Day before: Review your notes, prepare questions for the interviewer, and get a good night of rest.

Practice Databricks AI Engineer Interview with HireFlow AI — our AI adapts to Databricks's interview style and gives real-time feedback.