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Design Candidate Search And LLM Evaluation Tasks
Design Candidate Search and LLM Evaluation Tasks
This problem involves three main components for a Machine Learning Engineer role:
- Candidate Search System: Design a system that matches job descriptions to candidate profiles. The system should be able to find top candidates from a given job description.
- Unreliable Deterministic Comparator: Reason about a conceptual problem involving an unreliable deterministic comparator. The goal is to reduce the number of calls and the wall-clock time for comparisons.
- Autoregressive Language Model Mini-Project: Implement or discuss an autoregressive language model trained on a small text corpus. This mini-project should be runnable end-to-end.
Constraints & Assumptions:
- The candidate search system must match job descriptions to candidate profiles.
- The comparator question is conceptual and focuses on 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.
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?
AI interview prep
Core dilemma
Balancing the complexity of a large-scale candidate search system with the practical constraints of ML model development and evaluation, particularly when dealing with potentially noisy or limited data and computational resources.
Mid-level signals3
Senior signals3
Red flags3
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design Candidate Search And LLM Evaluation Tasks
Design Candidate Search and LLM Evaluation Tasks
This problem involves three main components for a Machine Learning Engineer role:
- Candidate Search System: Design a system that matches job descriptions to candidate profiles. The system should be able to find top candidates from a given job description.
- Unreliable Deterministic Comparator: Reason about a conceptual problem involving an unreliable deterministic comparator. The goal is to reduce the number of calls and the wall-clock time for comparisons.
- Autoregressive Language Model Mini-Project: Implement or discuss an autoregressive language model trained on a small text corpus. This mini-project should be runnable end-to-end.
Constraints & Assumptions:
- The candidate search system must match job descriptions to candidate profiles.
- The comparator question is conceptual and focuses on 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.
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?