Databricks AI Infrastructure System Design interview questions
Practise ai infrastructure system design for Databricks ML Platform Engineer roles. Design an inference platform at 10k requests per second, a 10,000-GPU training cluster, a job scheduler with preemption and checkpointing, a serverless GPU runtime with sub-second cold starts, a multi-tenant fine-tuning service, an eval pipeline. The whiteboard round at OpenAI, Anthropic, Baseten and Together. Questions tagged to Databricks appear first, followed by additional practice from our AI Infrastructure System Design track. Company tags indicate preparation relevance, not confirmed interview appearances. Confirm the current interview format with your recruiter.
PREPARATION CONTEXT · Model serving, fine-tuning and vector search on the platform. Sources and confidence in the Databricks guide →
AI Infrastructure System Design questions tagged to Databricks
Additional AI Infrastructure System Design practice
These questions cover the same topic and are not tagged to Databricks. Use them to practise applying the ideas in a different situation.
Concepts for AI Infrastructure System Design preparation
These concept explanations cover the ideas used in the questions. Check each card for its access level.
This collection includes practice questions such as "Design the pipeline that produces 15 trillion training tokens: ingest, dedup, tokenize, shard, serve. Throughput per stage.", "Design serving for 100 fine-tuned variants of one 70B base. Multi-LoRA on shared replicas or a replica per variant?", "Design the registry and deploy pipeline for model weights: 141 GB artifacts, versioning, distribution to a thousand nodes, integrity.". Company tags describe relevance to preparation; they do not confirm that Databricks asked these questions. Check each question for answer access.
Start with a question at your level, use the linked concepts to understand the mechanism, then practise explaining your decision and its limits. Review the company guide's sources and confidence notes, and confirm the current role and interview format with your recruiter.
Yes: Senior Machine Learning Engineer, GenAI Platform (Mosaic AI: serving, fine-tuning, vector search) in San Francisco and platform infrastructure roles, plus roles in its Bengaluru engineering centre; per 2026 postings.
Databricks publishes an official interview preparation guide for the general loop. For the platform roles, prepare backend system design with GPUs as the scheduled resource, multi-tenant serving and fine-tuning service design, and training data pipelines; we found no AI-infra-specific first-hand debrief and do not list unconfirmed rounds.
Other topics for Databricks preparation
Explore the other topics mapped to Databricks in our preparation guide.
Continue your Databricks interview preparation
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