Technical practice
Where would you apply AI beyond a chatbot in GoSats’ consumer product?
What to coverThe role asks for an AI-native layer that substantially improves the experience of turning everyday spending into Bitcoin and Gold. Suggested preparation: compare opportunities by user value, feasibility, trust risk and how you would measure improvement.
How would you design and evaluate intelligent wealth recommendations?
What to coverWealth recommendations are one opportunity named in the posting. Suggested preparation: explain what information a recommendation would need, how you would communicate uncertainty, and how you would test usefulness without overstating potential outcomes.
How would you decide when to send a personalized notification?
What to coverPersonalized notifications are another stated opportunity. Suggested preparation: discuss relevance, timing and frequency, including how you would detect notifications that are unhelpful or intrusive.
How would you assess an AI-assisted fraud detection feature?
What to coverThe role explicitly mentions fraud detection. Suggested preparation: address false positives versus missed fraud, review or escalation for uncertain cases, and measures of user impact.
What would make smart spend analysis useful to GoSats users?
What to coverSmart spend analysis is listed as a potential feature for a product built around everyday spending. Suggested preparation: describe a useful insight, the spending data it would depend on, and how you would handle incomplete or misclassified transactions.
How would you prototype an AI feature before involving engineering?
What to coverThe posting calls for hands-on prototypes with tools such as Claude, Codex and Cursor before scaling development to engineering. Suggested preparation: outline a small testable prototype, the feedback you would seek, and what you would document for a handoff.
When would an LLM be the wrong choice for a product feature?
What to coverUnderstanding LLM capabilities, limitations and cost economics is a stated requirement. Suggested preparation: compare an LLM with a simpler approach on reliability, latency and per-use cost, and explain what evidence would change your choice.
How would you evaluate whether a shipped AI feature improves the consumer experience?
What to coverThe role calls for building consumer AI features rather than AI for its own sake. Suggested preparation: identify a user outcome, a measure of feature quality and a way to detect harmful or confusing results after launch.
When might retrieval, a vector database or fine-tuning be worth considering?
What to coverExperience with vector databases, RAG or fine-tuning is listed as a bonus, not a requirement. Suggested preparation: compare the problems each might address, their maintenance and cost trade-offs, and when none would be justified.
Behavioural practice
Tell me about an AI-powered consumer feature you helped ship.
What to coverThe posting asks for experience building and shipping such a feature. Use a real situation, your own product decisions and actions, and the observed result; distinguish your contribution from the team's.
Describe a time you built a useful prototype with limited engineering support.
What to coverThe role emphasizes rapid, hands-on prototyping without engineering dependency. Use a real situation to explain what you built yourself, what you learned from it, and what happened next.
Tell me about a time user feedback changed a product design you favoured.
What to coverStrong product taste and design sensibility are stated requirements. Choose a real example and explain the feedback, your response, and the resulting change or outcome.
Describe a handoff from product prototype to an engineering team.
What to coverThe posting describes prototyping before scaling development to engineering. If you have a relevant example, explain the situation, how you conveyed evidence and limitations, and the result of the handoff.
Tell me about a time a product feature produced an unexpected or poor user outcome.
What to coverThe role's proposed AI uses include recommendations, notifications and fraud detection, where mistakes could affect users. If you have a real example, describe how you identified the problem, what you did, and what changed as a result.