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Design Candidate Search And LLM Evaluation Tasks

MercorMachine Learning Engineer · Onsite
MediumMachine Learning EngineerOnsiteML System Design

Last updated

Prepare for an MLE system design interview that combines candidate search, ranking, comparator reliability, and language-model fundamentals. The prompt covers retrieval, ranking feedback loops, top-k reasoning, transformer concepts, and decoding trade-offs without requiring proprietary hiring data.

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.

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?

Hints

Hint 1

Hint 1: Start by naming the core entities, constraints, and success criteria.

Hint 2

Hint 2: Make the trade-offs explicit before going deep on implementation details.

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?