Technical practice
How would you take a generative model from preprocessing through evaluation and deployment for an international market?
What to coverThe posting describes ownership of pipeline components and culturally and linguistically adaptable local experiences. Suggested preparation: explain how you would validate input data, test language-specific behavior, integrate the model into a workflow, and check results before and after deployment.
How do tokenization and decoding choices affect an NLP system across languages?
What to coverThe posting explicitly names tokenization, language modeling, and decoding. Suggested preparation: discuss how these choices affect output quality and efficiency across languages, and how you would investigate poor results in a particular language.
How would you evaluate an NLP classification component used in a production workflow?
What to coverClassification is among the applied NLP techniques named in the posting. Suggested preparation: explain how you would select representative labeled examples, inspect error patterns across languages, and balance quality measures against operational constraints.
How would you design and diagnose a RAG workflow?
What to coverThe posting requires applied experience with RAG architectures. Suggested preparation: distinguish retrieval errors from generation errors, explain how you would evaluate each stage, and discuss trade-offs between relevance, latency, and answer quality.
When would you use a multi-agent system rather than a single agent?
What to coverThe posting names modern agentic and multi-agent systems. Suggested preparation: compare task division and coordination benefits with added complexity, then describe how you would trace a failed run to a particular decision or interaction.
How would you make an ML or agent evaluation harness repeatable?
What to coverThe role calls for experience building harnesses and prefers controlled, deterministic evaluation environments. Suggested preparation: cover stable datasets and configurations, controlled dependencies, and how you would separate reproducible regressions from genuine variation in model behavior.
How would you score an agent when its final answer alone misses important failures?
What to coverThe preferred qualifications mention metrics, scoring methods, and trajectory and outcome analysis. Suggested preparation: explain what you would inspect along the trajectory as well as at the outcome, and how you would check whether a score reflects useful behavior.
How would you improve a production AI agent using feedback and usage data?
What to coverThe posting prefers experience operating reliable, scalable, safe agents and iterating from user feedback and usage data. Suggested preparation: describe how you would identify a recurring failure, evaluate a proposed change, and watch for reliability or safety regressions.
How would you integrate an ML model into a maintainable production codebase?
What to coverThe minimum qualifications specify Python plus one of C, C++, Objective-C, or Swift, and emphasize clean, tested code and production integration. Suggested preparation: discuss interfaces, tests, code review, and how OS or performance constraints could influence your implementation.
How would you debug a production ML performance regression, and where could coding assistants help?
What to coverThe posting emphasizes debugging and performance and names practical use of Claude, Cursor, and Codex. Suggested preparation: walk through isolating a regression with measurements and tests, then explain how you would verify any assistant-generated diagnosis or code before relying on it.
Behavioural practice
Tell me about a time you worked with ML and software engineers to ship a model-backed feature.
What to coverThe role involves working alongside both ML and software engineers on production integration. Suggested preparation: give a real situation, your specific coding and coordination actions, and the observed result without treating the team's work as solely your own.
Describe a production issue you personally helped troubleshoot.
What to coverThe posting emphasizes hands-on debugging, systems thinking, and production troubleshooting. Suggested preparation: use a real situation to explain how you narrowed the problem, what you changed or escalated, and what evidence showed the outcome.
Tell me about a time the platform or component you were working on changed.
What to coverThe description says platforms, frameworks, and components can change over time. Suggested preparation: describe the change, how you adapted your approach while preserving quality, and the result.
Give an example of improving code quality through review or technical discussion.
What to coverThe posting names code reviews, technical discussions, maintainable code, and sharing knowledge. Suggested preparation: describe a specific disagreement or quality concern, your contribution to resolving it, and the effect on the code or team.
Tell me about a time feedback or evaluation results changed your priorities.
What to coverThe preferred qualifications include using user feedback and usage data to improve production AI agents, while the role emphasizes evaluation and results. Suggested preparation: give a real situation, how you weighed the evidence and communicated a decision, and the measurable or observable result.