Databricks

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AI infrastructure · International · Loop reviewed 2026-09-26

An older guide describes a timed coding screen and role-specific final rounds, with a take-home case for product managers. Treat the details as dated.

On this page: Interview loop · Questions (8)

Roles covered

• AI Engineering: Software engineer (general interview baseline), AI / LLM engineer
• AI Leadership: Product manager (general interview baseline)

Interview loop

Stage	What happens	Basis
Recruiter screen	Background, fit with the role and why Databricks.	† prep guide or compilation Get a Job at Databricks: Interview Process and Top Questions, Aced (formerly Exponent), retrieved 2026-09-26
Technical screen	About 70 minutes of coding on CodeSignal.	† prep guide or compilation Get a Job at Databricks: Interview Process and Top Questions, Aced (formerly Exponent), retrieved 2026-09-26
Final round	Four or five hour-long interviews mixing behavioral, technical and domain rounds; product managers also prepare and present a take-home case.	† prep guide or compilation Get a Job at Databricks: Interview Process and Top Questions, Aced (formerly Exponent), retrieved 2026-09-26
Questions reported for AI work	A compilation attributes to Databricks questions on permission-aware enterprise RAG, Text-to-SQL over large warehouses and diagnosing latency regressions.	† prep guide or compilation AI Engineering Interview Questions Company Wise, Outcome School, published 2026-09-19, retrieved 2026-09-26

Coding: unknown. The older secondary guide describes coding; company or first-hand confirmation is absent here. (Get a Job at Databricks: Interview Process and Top Questions, Aced (formerly Exponent), retrieved 2026-09-26)

Questions reported for Databricks

Inference, serving and cost

• After a deployment, p99 latency doubles although model weights are unchanged. How do you isolate the cause?
  • Applied scenario · Asked at: Amazon †, Databricks †, OpenAI †, Perplexity † · ✍ Answer
  • Tests: Whether diagnosis separates queueing, scheduling, dependencies and traffic changes.
  • A strong answer covers:
    • Compare traffic mix, queue time and per-stage latency before and after release.
    • Inspect cache hit rates, batching, dependency tails and resource saturation.
    • Reproduce one hypothesis at a time and use a rollback or canary to verify causality.
  • Read: The Tail at Scale (Dean and Barroso, Google Research)

RAG and retrieval

• Design retrieval that enforces the source system's access rights, including permission changes and shared caches.
  • System design · Asked at: Databricks †, Microsoft †, Palantir † · ✍ Answer
  • Tests: Whether access checks cover the full path to generated output.
  • A strong answer covers:
    • Propagate identity and enforce authorisation before content reaches the model.
    • Keep permission updates, deletions and cache keys consistent with the policy.
    • Test denied access and revocation paths; citations and logs need the same boundary.
  • Read: Document-level access control (Microsoft Learn)

Fine-tuning and post-training

• Choose between prompt changes, retrieval and finetuning for a failing AI feature; include data freshness, latency and total cost.
  • Applied scenario · Asked at: Databricks †, Microsoft †, OpenAI † · ✍ Answer
  • Tests: Whether the intervention matches the observed failure mode.
  • A strong answer covers:
    • Classify missing knowledge, poor instructions and unwanted behaviour separately.
    • Establish a prompt baseline; test retrieval for fresh attributable knowledge.
    • Use finetuning when representative examples justify it, then compare lifecycle cost and held-out quality.
  • Read: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., arXiv) · LoRA: Low-Rank Adaptation of Large Language Models (Hu et al., arXiv)

AI system design

• Design a knowledge assistant over ten million enterprise documents with per-user permissions and a continuously changing corpus.
  • System design · Asked at: Amazon †, Databricks †, Microsoft †, OpenAI † · ✍ Answer
  • Tests: Whether ingestion, retrieval, access control and serving form an operable system.
  • A strong answer covers:
    • Clarify query volume, freshness and permission semantics before sizing.
    • Separate ingestion and index versions from query-time authorisation and retrieval.
    • Evaluate grounded answers and test revocation, stale data and partial outages.
  • Read: Document-level access control (Microsoft Learn) · Contextual Retrieval in AI Systems (Anthropic)
• Design natural-language querying over a warehouse with thousands of tables, from schema selection to safe query execution.
  • System design · Asked at: Databricks †, Palantir † · ✍ Answer
  • Tests: Whether semantic correctness and query permissions constrain generation.
  • A strong answer covers:
    • Retrieve a small candidate schema from catalog metadata, governed metric definitions and verified example queries.
    • Run generated SQL read-only under the requesting user's own warehouse permissions, with cost, row and time limits.
    • Compare result sets with verified queries on real questions, and measure schema-retrieval recall separately.
  • Read: OWASP Top 10 for LLM Applications (OWASP Gen AI Security Project) · Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows (Lei et al., arXiv)

AI product strategy and metrics

• How do you prioritise a product roadmap when several valuable opportunities compete?
  • Product case · Senior · Asked at: Databricks †
  • Tests: Whether prioritisation exposes assumptions and opportunity cost.
  • A strong answer covers:
    • Tie each opportunity to its user, one outcome metric and the assumption it rests on.
    • Compare expected value with cost and confidence, and name what each choice delays or forgoes.
    • Fund cheap tests of the shakiest assumptions first, and set a date to revisit the order.
  • Read: Product Management - Leadership (GitLab Handbook) · Measuring the User Experience on a Large Scale: User-Centered Metrics for Web Applications (Rodden, Hutchinson and Fu, Google Research)

Applied and customer scenarios

• A customer insists on fine-tuning their own model on support tickets, while you suspect retrieval would be enough. How do you proceed?
  • Applied scenario · Senior · Asked at: Databricks † · ✍ Answer
  • Tests: Whether you investigate the need without turning architecture preferences into a sales argument.
  • A strong answer covers:
    • Ask what ownership is meant to achieve: behaviour, knowledge, cost or control.
    • Build a representative task set and compare plausible approaches.
    • Explain data governance, maintenance and migration costs before recommending.
  • Read: Rules of Machine Learning (Google for Developers) · Building Effective AI Agents (Anthropic)
• A deployed agent's base model will be retired in 60 days. How would you migrate it without losing quality?
  • Applied scenario · Senior · Asked at: Databricks † · ✍ Answer
  • Tests: Whether provider lifecycle risk is handled through reproducible evaluation and controlled change.
  • A strong answer covers:
    • Inventory affected workflows, tool contracts and model-specific behaviour.
    • Compare candidates against representative recorded tasks and failure cases.
    • Plan staged migration, rollback within the available window and customer communication.
  • Read: Demystifying evals for AI agents (Anthropic) · Site Reliability Engineering (Google)

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