Careerflow interview question — September 2026

Interview question

Design a low-cost subsystem that classifies every sentence of a job description so downstream AI tools can use only relevant content and reduce their input-token cost. The API input is a single raw text blob without HTML, Markdown, newlines, or paragraph boundaries. Return a JSON object mapping each sentence to one of five classes: roles and responsibilities, equal opportunity, about the company, required qualifications, and benefits. Assume every sentence belongs to a class. The subsystem runs on every API call, must respond in under one second, and must have very low operating cost; initial setup cost is not constrained. It must support job descriptions from arbitrary industries and roles. Walk through the system design; code is not required.

Follow-up questions

Once the job description has been split into sentences, what is the next step?
If you use cosine similarity, what exactly are the two entities being compared? How would this map sentences to a class such as roles and responsibilities across very different jobs, such as software engineer and chef?
If you create industry subcategories or example descriptions, how would your design cover jobs outside those categories and new roles that emerge over time? How many descriptions would you need?
Walk through how your classifier would process the sentence “Three years building and shipping models in Python in a fast-paced environment” using the three classes roles and responsibilities, about the company, and required qualifications. What would the sentence be compared against?
Give a concrete example of a class-description sentence that would work across all job descriptions, rather than only one job or industry.
If you propose a small model, what kind of model do you mean, how would it work, and how would you deploy it?
How would your model choice balance response accuracy and hallucinations against inference latency and operating cost?
If you fine-tune a model for this task, what kind of small model would you select?
Why would you choose your proposed language-model approach instead of an LSTM or a supervised neural-network classifier trained on labeled sentences?
You raised multilingual input as a concern. Why would a translation layer help your approach but not also help a neural-network classifier?
When using hypothetical document embeddings to generate additional context for a query, what information is that generated context based on?