Technical practice
How would you choose an LLM architecture for a natural language processing task?
What to coverSuggested preparation: explain how you would translate project requirements into architecture choices for processing and generating text. Discuss trade-offs among model capability, computational cost, performance and scalability.
How would you plan training on a large collection of unlabeled text?
What to coverSuggested preparation: describe how you would select a training approach and assess whether the data supports the intended NLP task. Explain what you would monitor during training and how you would respond if results fell short.
How would you tune model parameters to improve accuracy without sacrificing efficiency?
What to coverSuggested preparation: identify the parameters you would consider changing and how you would compare experiments. Explain how you would decide whether an accuracy gain justifies its compute or latency cost.
How would you evaluate whether an LLM meets a project's requirements?
What to coverSuggested preparation: describe how you would test relevant language capabilities and examine model behavior, rather than relying on one aggregate result. Explain how findings would inform the next iteration.
What would you examine if an LLM performed well in development but could not scale?
What to coverSuggested preparation: distinguish model-related bottlenecks from deployment or system bottlenecks. Explain how you would measure the issue and weigh possible improvements against their effect on model quality.
How would you troubleshoot unexpected model behavior or a performance regression?
What to coverSuggested preparation: walk through a method for reproducing the issue, comparing results with a baseline and narrowing down possible causes. Describe how you would verify that a change fixes the problem without introducing another one.
How would you assess language-model frameworks and tools for a project?
What to coverSuggested preparation: compare how well available tools support the required architecture, training, evaluation and deployment work. Make clear that the posting names no specific framework, so the question is about your evaluation criteria rather than a required tool.
How would you lead deployment and integration of an LLM into a broader application?
What to coverSuggested preparation: explain how you would define the model's interface and coordinate decisions with teams responsible for the surrounding system. Discuss how you would check that the integrated model still meets project requirements.
How would you decide when to iterate on a deployed model as NLP needs change?
What to coverSuggested preparation: describe signals that would prompt reevaluation and how you would prioritize changes. Explain how you would compare an updated model with the existing one before making a deployment decision.
Behavioural practice
Tell me about a consequential technical decision you owned for a team.
What to coverSuggested preparation: use a real situation to explain the decision, the alternatives you considered and the action you took. State the result and what you learned about taking responsibility for team decisions.
Describe a time you helped a team member develop technical expertise.
What to coverSuggested preparation: give a real example of the person's learning need, how you shared knowledge or mentored them, and the result. Focus on your actions without claiming credit for work you did not do.
Tell me about a cross-functional decision on integrating a model into a larger system.
What to coverSuggested preparation: describe the situation, the differing requirements and how you worked with other teams to reach a decision. Explain the result and any trade-off you had to communicate.
Describe a problem you solved that affected multiple teams.
What to coverSuggested preparation: use a real example to show how you identified the wider impact and coordinated a response beyond your immediate team. Explain the outcome and how you determined whether the solution worked.
Tell me about a time evaluation results caused you to change an ML approach.
What to coverSuggested preparation: describe the original goal, the evidence that challenged your approach and the action you took. Explain the result, including any remaining limitation or trade-off.