Design Candidate Search And LLM Evaluation Tasks

Prepare for an MLE onsite with three themes: finding top candidates from a job description, reasoning about an unreliable deterministic comparator, and implementing or discussing an autoregressive language model trained on a small text corpus. The system design focus is candidate search; the ML follow-ups include transformer architecture, normalization, self-attention, positional embeddings, and decoding strategies such as greedy, beam search, top-k, and top-p. ```hint Hint 1 Start by naming the core entities, constraints, and success criteria. ``` ```hint Hint 2 Make the trade-offs explicit before going deep on implementation details. ``` ### Constraints & Assumptions - The candidate search system should match job descriptions to candidate profiles. - The comparator question is conceptual and asks about reducing calls and wall-clock time. - The LLM mini-project is small-scale and should run end to end. - Do not assume proprietary hiring data or hidden labels. ### Clarifying Questions to Ask - What candidate data is available? - What labels or feedback can train the ranking system? - Should search prioritize recall, precision, fairness, or recruiter efficiency? - What does the comparator compare and how are errors detected? - What model size and training budget are expected for the LLM mini-project? ### What a Strong Answer Covers ```premium-lock What a Strong Answer Covers ``` ### Follow-up Questions - How would you evaluate candidate search offline and online? - How would you reduce wall-clock time when each comparison takes five seconds? - How would you debug an LLM that memorizes the training text? - When would top-p decoding be preferable to beam search?