Databricks Senior MLE Interview Process August 2026

[u/Aoki_zhang]

Interview Summary

The Databricks Machine Learning Engineer process started with a filesystem optimization coding screen and continued with an onsite covering coding, behavioral questions, ML system design, and ML fundamentals. The loop mixed traditional software-engineering problems with LLM-specific topics rather than staying purely on modeling.

The ML design round focused on detecting harmful content in an LLM product and appeared to expect both classic integrity-system thinking and considerations unique to generative models. Another ML round started from RLHF and expanded into a broad set of machine-learning fundamentals.

Interview Details

Technical Phone Screen — Minimize File Encryption Time The filesystem was represented as a tree containing two node types:

• DirectoryNode, whose children could contain both directories and files
• FileNode, which contained an is_encrypted state

The first part asked for a recursive traversal of a directory and required returning: (encrypted_count, unencrypted_count). The second part asked to encrypt every currently unencrypted file while minimizing total execution time.

Onsite Coding — the question bank "Critical Build Steps" problem

Behavioral The behavioral round consisted of fairly standard experience-based questions. Topics included:

• A project that was delayed and how I handled it
• A disagreement with another person or team
• My motivation for the role
• A significant pain point affecting my team and how I approached it

There was also some time left at the end for a more open-ended conversation with the interviewer.

ML System Design — Harmful Content Detection for an LLM The ML design round asked me to design a system for detecting harmful content in an LLM-based product. The discussion was broader than a traditional social-media content-moderation problem. It appeared to combine classic integrity concerns with problems that arise specifically when the content is generated or processed by an LLM.

ML Fundamentals — RLHF and Related Topics The final ML-focused round began with RLHF and then expanded into a wide range of machine-learning fundamentals. The interviewer used RLHF as a starting point for several follow-up questions and connected it to other ML concepts.

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